School of Computing

Publications by Prof Alex Freitas

Also view these in the Kent Academic Repository

Article
Ribeiro, C. and Freitas, A. A. (2021) “A data-driven missing value imputation approach for longitudinal datasets”, Artificial Intelligence Review. Springer. doi: 10.1007/s10462-021-09963-5.
Tighe, D., Fabris, F. and Freitas, A. (2021) “Machine learning methods applied to audit of surgical margins after curative surgery for head and neck cancer”, British Journal of Oral and Maxillofacial Surgery. Elsevier, pp. 209-216. doi: 10.1016/j.bjoms.2020.08.041.
Palmer, D., Fabris, F., Doherty, A., Freitas, A. A. and de Magalhaes, J. P. (2021) “Ageing transcriptome meta-analysis reveals similarities between key mammalian tissues”, Aging. Impact Journals, pp. 3313-3341. doi: 10.18632/aging.202648.
Pavelski, L., Delgado, M., Kessaci, M. and Freitas, A. A. (2020) “Stochastic local search and parameters recommendation: a case study on flowshop problems”, International Transactions in Operational Research. Wiley, pp. 1-26. doi: 10.1111/itor.12922.
Neumann, N. M., Plastino, A., Pinto Junior, J. A. and Freitas, A. A. (2020) “A study on the statistical evaluation of classifiers”, Knowledge Engineering Review. Cambridge University Press, pp. 1-26. doi: 10.1017/S0269888920000417.
Basgalupp, M., Barros, R., de Sá, A., Pappa, G., Mantovani, R., de Carvalho, A. and Freitas, A. (2020) “An extensive experimental evaluation of automated machine learning methods for recommending classification algorithms”, Evolutionary Intelligence. Springer, pp. 1895-1914. doi: 10.1007/s12065-020-00463-z.
da Silva, P. N., Plastino, A. and Freitas, A. A. (2020) “Prioritizing positive feature values: a new hierarchical feature selection method”, Applied Intelligence. Springer. doi: 10.1007/s10489-020-01782-5.
Fabris, F., Palmer, D., de Magalhaes, J. P. and Freitas, A. A. (2020) “Comparing enrichment analysis and machine learning for identifying gene properties that discriminate between gene classes”, Briefings in Bioinformatics. Oxford University Press, pp. 803-814. doi: 10.1093/bib/bbz028.
Da Silva, P. N., Plastino, A., Fabris, F. and Freitas, A. A. (2020) “A Novel Feature Selection Method for Uncertain Features: An Application to the Prediction of Pro-/Anti- Longevity Genes”, IEEE/ACM Transactions on Computational Biology and Bioinformatics. IEEE. doi: 10.1109/TCBB.2020.2988450.
Xavier-Junior, J. C., Freitas, A. A., Ludermir, T. B., Feitosa-Neto, A. and Barreto, C. A. (2020) “An evolutionary algorithm for automated machine learning focusing on classifier ensembles: an improved algorithm and extended results”, Theoretical Computer Science. Elsevier, pp. 1-18. doi: 10.1016/j.tcs.2019.12.002.
Fabris, F., Palmer, D., Salama, K. M., de Magalhaes, J. P. and Freitas, A. A. (2019) “Using deep learning to associate human genes with age-related diseases”, Bioinformatics. Oxford University Press, pp. 2202-2208. doi: 10.1093/bioinformatics/btz887.
Cramer, S., Kampouridis, M., Freitas, A. A. and Alexandridis, A. (2019) “Stochastic Model Genetic Programming: Deriving Pricing Equations for Rainfall Weather Derivatives”, Swarm and Evolutionary Computation. Elsevier, pp. 184-200. doi: 10.1016/j.swevo.2019.01.008.
Freitas, A. A. (2019) “Investigating the Role of Simpson’s Paradox in the Analysis of Top-Ranked Features in High-Dimensional Bioinformatics Datasets”, Briefings in Bioinformatics. Oxford University Press, pp. 421-428. doi: 10.1093/bib/bby126.
Tighe, D., Lewis-Morris, T. and Freitas, A. A. (2019) “Machine learning methods applied to audit of surgical outcomes after treatment for cancer of the head and neck”, British Journal of Oral and Maxillofacial Surgery. Elsevier, pp. 771-777. doi: 10.1016/j.bjoms.2019.05.026.
Zhou, N., Jiang, Y., Bergquist, T. R., Lee, A. J., Kacsoh, B. Z., Crocker, A. W., Lewis, K. A., Georghiou, G., Nguyen, H. N., Hamid, M. N., Davis, L., Dogan, T., Atalay, V., Rifaioglu, A. S., Dalkıran, A., Cetin Atalay, R., Zhang, C., Hurto, R. L., Freddolino, P. L., Zhang, Y., Bhat, P., Supek, F., Fernández, J. M., Gemovic, B., Perovic, V. R., Davidović, R. S., Sumonja, N., Veljkovic, N., Asgari, E., Mofrad, M. R., Profiti, G., Savojardo, C., Martelli, P. L., Casadio, R., Boecker, F., Schoof, H., Kahanda, I., Thurlby, N., McHardy, A. C., Renaux, A., Saidi, R., Gough, J., Freitas, A. A., Antczak, M., Fabris, F., Wass, M. N., Hou, J., Cheng, J., Wang, Z., Romero, A. E., Paccanaro, A., Yang, H., Goldberg, T., Zhao, C., Holm, L., Törönen, P., Medlar, A. J., Zosa, E., Borukhov, I., Novikov, I., Wilkins, A., Lichtarge, O., Chi, P.-H., Tseng, W.-C., Linial, M., Rose, P. W., Dessimoz, C., Vidulin, V., Dzeroski, S., Sillitoe, I., Das, S., Lees, J. G., Jones, D. T., Wan, C., Cozzetto, D., Fa, R., Torres, M., Warwick Vesztrocy, A., Rodriguez, J. M., Tress, M. L., Frasca, M., Notaro, M., Grossi, G., Petrini, A., Re, M., Valentini, G., Mesiti, M., Roche, D. B., Reeb, J., Ritchie, D. W., Aridhi, S., Alborzi, S. Z., Devignes, M.-D., Koo, D. C. E., Bonneau, R., Gligorijević, V., Barot, M., Fang, H., Toppo, S., Lavezzo, E., Falda, M., Berselli, M., Tosatto, S. C., Carraro, M., Piovesan, D., Ur Rehman, H., Mao, Q., Zhang, S., Vucetic, S., Black, G. S., Jo, D., Suh, E., Dayton, J. B., Larsen, D. J., Omdahl, A. R., McGuffin, L. J., Brackenridge, D. A., Babbitt, P. C., Yunes, J. M., Fontana, P., Zhang, F., Zhu, S., You, R., Zhang, Z., Dai, S., Yao, S., Tian, W., Cao, R., Chandler, C., Amezola, M., Johnson, D., Chang, J.-M., Liao, W.-H., Liu, Y.-W., Pascarelli, S., Frank, Y., Hoehndorf, R., Kulmanov, M., Boudellioua, I., Politano, G., Di Carlo, S., Benso, A., Hakala, K., Ginter, F., Mehryary, F., Kaewphan, S., Björne, J., Moen, H., Tolvanen, M. E., Salakoski, T., Kihara, D., Jain, A., Šmuc, T., Altenhoff, A., Ben-Hur, A., Rost, B., Brenner, S. E., Orengo, C. A., Jeffery, C. J., Bosco, G., Hogan, D. A., Martin, M. J., O’Donovan, C., Mooney, S. D., Greene, C. S., Radivojac, P. and Friedberg, I. (2019) “The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens”, Genome Biology. BMC. doi: 10.1186/s13059-019-1835-8.
Cramer, S., Kampouridis, M. and Freitas, A. A. (2018) “Decomposition Genetic Programming: An Extensive Evaluation on Rainfall Prediction in the Context of Weather Derivatives”, Applied Soft Computing. Elsevier, pp. 208-224. doi: 10.1016/j.asoc.2018.05.016.
Fabris, F., Doherty, A., Palmer, D., de Magalhães, J. P. and Freitas, A. A. (2018) “A new approach for interpreting Random Forest models and its application to the biology of ageing”, Bioinformatics. Oxford University Press, pp. 2449-2456. doi: 10.1093/bioinformatics/bty087.
de Oliveira, L. L., Freitas, A. A. and Tinós, R. (2017) “Multi-objective genetic algorithms in the study of the genetic code’s adaptability”, Information Sciences. Elsevier, pp. 48-61. doi: 10.1016/j.ins.2017.10.022.
Salama, K. M., Abdelbar, A. M., Helal, A. and Freitas, A. A. (2017) “Instance-based classification with ant colony optimization”, Intelligent Data Analysis. IOS Press, pp. 913-944. doi: 10.3233/IDA-160031.
Barardo, D. G., Newby, D., Thornton, D., Ghafourian, T., Pedro de Magalhães, J. and Freitas, A. A. (2017) “Machine learning for predicting lifespan-extending chemical compounds”, Aging, pp. 1721-1737. doi: 10.18632/aging.101264.
Cramer, S., Kampouridis, M., Freitas, A. A. and Alexandridis, A. (2017) “An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives”, Expert Systems with Applications. Elsevier, pp. 169-181. doi: 10.1016/j.eswa.2017.05.029.
Ribeiro, C., Brito, L., Nobre, C., Freitas, A. A. and Zarate, L. (2017) “A revision and analysis of the comprehensiveness of the main longitudinal studies of human ageing for data mining research”, WIREs : Data Mining and Knowledge Discovery. Wiley. doi: 10.1002/widm.1202.
Fabris, F., Freitas, A. A. and de Magalhaes, J. P. (2017) “A Review of Supervised Machine Learning Applied to Ageing Research”, Biogerontology. Springer, pp. 171-188. doi: 10.1007/s10522-017-9683-y.
Wan, C. and Freitas, A. A. (2017) “An empirical evaluation of hierarchical feature selection methods for classification in bioinformatics datasets with gene ontology-based features”, Artificial Intelligence Review. Springer, pp. 201-240. doi: 10.1007/s10462-017-9541-y.
Aniceto, N., Freitas, A. A., Bender, A. and Ghafourian, T. (2016) “A novel applicability domain technique for mapping predictive reliability across the chemical space of a QSAR: reliability-density neighbourhood”, Journal of Cheminformatics. Springer. doi: 10.1186/s13321-016-0182-y.
Otero, F. E. and Freitas, A. A. (2016) “Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results”, Evolutionary Computation. MIT Press, pp. 385-409. doi: 10.1162/EVCO_a_00155.
Fernandes, M., Wan, C., Tacutu, R., Barardo, D., Rajput, A., Wang, J., Thoppil, H., Thornton, D., Yang, C., Freitas, A. A. and de Magalhaes, J. P. (2016) “Systematic analysis of the gerontome reveals links between aging and age-related diseases”, Human Molecular Genetics. Oxford University Press, pp. 4804-4818. doi: 10.1093/hmg/ddw307.
Aniceto, N., Freitas, A. A., Bender, A. and Ghafourian, T. (2016) “Simultaneous prediction of four ATP-binding cassette transporters substrates using multi-label QSAR”, Molecular Informatics. Wiley, pp. 514-528. doi: 10.1002/minf.201600036.
Fabris, F. and Freitas, A. A. (2016) “New KEGG pathway-based interpretable features for classifying ageing-related mouse proteins”, Bioinformatics. Oxford University Press, pp. 2988-2995. doi: 10.1093/bioinformatics/btw363.
Fabris, F., Freitas, A. A. and Tullet, J. M. (2015) “An Extensive Empirical Comparison of Probabilistic Hierarchical Classifiers in Datasets of Ageing-Related Genes”, IEEE/ACM Transactions on Computational Biology and Bioinformatics. IEEE, pp. 1045-1058. doi: 10.1109/TCBB.2015.2505288.
Wan, C., Freitas, A. A. and de Magalhaes, J. P. (2015) “Predicting the pro-longevity or anti-longevity effect of model organism genes with new hierarchical feature selection methods”, IEEE/ACM Transactions on Computational Biology and Bioinformatics. IEEE, pp. 262-275. doi: 10.1109/TCBB.2014.2355218.
Cerri, R., Pappa, G. L., de Carvalho, A. C. and Freitas, A. A. (2015) “An extensive evaluation of decision-tree based hierarchical multilabel classification methods and performance measures”, Computational Intelligence, pp. 1-46.
Newby, D., Freitas, A. A. and Ghafourian, T. (2015) “Decision trees to characterise the roles of permeability and solubility on the prediction of oral absorption.”, European Journal of Medicinal Chemistry, pp. 751-765.
Newby, D., Freitas, A. A. and Ghafourian, T. (2015) “Comparing Multi-Label Classification Methods for Provisional Biopharmaceutics Class Prediction.”, Molecular Pharmaceutics, pp. 87-102.
Freitas, A. A., Limbu, K. and Ghafourian, T. (2015) “Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients”, Journal of Cheminformatics. doi: 10.1186/s13321-015-0054-x.
Salama, K. M. and Freitas, A. A. (2015) “Ant colony algorithms for constructing Bayesian multi-net classifiers”, Intelligent Data Analysis. IOS Press, pp. 233-257. doi: 10.3233/IDA-150715.
Salama, K. M. and Freitas, A. A. (2014) “Classification with cluster-based Bayesian multi-nets using Ant Colony Optimisation”, Swarm and Evolutionary Computation. Elsevier, pp. 54-70. doi: 10.1016/j.swevo.2014.05.001.
Salama, K. M. and Freitas, A. A. (2014) “ABC-Miner+: constructing Markov blanket classifiers with ant colony algorithms”, Memetic Computing. Springer, pp. 183-206. doi: 10.1007/s12293-014-0138-6.
Freitas, A. A. (2014) “Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms”, Genetic Programming and Evolvable Machines. Springer, pp. 3-35. doi: 10.1007/s10710-013-9186-9.
Basgalupp, M. P., Barros, R. C., de Carvalho, A. C. and Freitas, A. A. (2014) “Evolving Decision Trees with Beam Search-based Initialization and Lexicographic Multi-Objective Evaluation”, Information Sciences, pp. 160-181. doi: 10.1016/j.ins.2013.07.025.
Paes, B., Plastino, A. and Freitas, A. A. (2014) “Exploring attribute selection in hierarchical classification.”, Journal of Information and Data Management. Sociedade Brasileira de Computacao, pp. 124-133.
Barros, R., Basgalupp, M., Freitas, A. A. and de Carvalho, A. (2013) “Evolutionary design of decision-tree algorithms tailored to microarray gene expression data sets”, IEEE Transactions on Evolutionary Computation. IEEE, pp. 873-892. doi: 10.1109/TEVC.2013.2291813.
Salama, K. M. and Freitas, A. A. (2013) “Learning Bayesian network classifiers using ant colony optimization”, Swarm Intelligence, pp. 229-254. doi: 10.1007/s11721-013-0087-6.
Freitas, A. A. (2013) “Comprehensible classification models - a position paper”, ACM SIGKDD Explorations. ACM, pp. 1-10. Available at: http://www.kdd.org/explorations.
Otero, F. E., Freitas, A. A. and Johnson, C. G. (2013) “A new sequential covering strategy for inducing classification rules with ant colony algorithms”, IEEE Transactions on Evolutionary Computation. IEEE Press, pp. 64-76. doi: 10.1109/TEVC.2012.2185846.
Salama, K. M., Abdelbar, A. M., Otero, F. E. and Freitas, A. A. (2013) “Utilizing multiple pheromones in an ant-based algorithm for continuous-attribute classification rule discovery.”, Applied Soft Computing. Elsevier Science, pp. 667-675. doi: 10.1016/j.asoc.2012.07.026.
Newby, D., Freitas, A. A. and Ghafourian, T. (2013) “Coping with unbalanced class data sets in oral absorption models”, Journal of Chemical Information Modeling. ACS, pp. 461-474. doi: 10.1021/ci300348u.
Barros, R. C., Basgalupp, M. P., Freitas, A. A. and de Carvalho, A. C. (2013) “Automatic Design of Decision-Tree Algorithms with Evolutionary Algorithms”, Evolutionary Computation. MIT Press, pp. 659-684. doi: 10.1162/EVCO_a_00101.
Newby, D., Freitas, A. A. and Ghafourian, T. (2013) “Pre-processing feature selection for improved C&RT models for oral absorption”, Journal of Chemical Information and Modeling. ACS, pp. 2730-2742. doi: 10.1021/ci400378j.
Ghafourian, T., Freitas, A. A. and Newby, D. (2012) “The impact of training set data distributions for modelling of passive intestinal absorption.”, International Journal of Pharmaceutics. Elsevier, pp. 711-720. doi: 10.1016/j.ijpharm.2012.07.041.
Otero, F. E., Freitas, A. A. and Johnson, C. G. (2012) “Inducing decision trees with an ant colony optimization algorithm”, Applied Soft Computing, pp. 3615-3626. doi: 10.1016/j.asoc.2012.05.028.
Barros, R. C., Basgalupp, M. P., de Carvalho, A. C. and Freitas, A. A. (2012) “A survey of evolutionary algorithms for decision-tree induction”, IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews. IEEE, pp. 291-312. doi: 10.1109/TSMCC.2011.2157494.
Paes, B. C., Plastino, A. and Freitas, A. A. (2012) “Improving local per level hierarchical classification.”, Journal of Information and Data Management. Sociedade Brasileira de Computacao, pp. 394-409.
Salama, K. M., Abdelbar, A. M. and Freitas, A. A. (2011) “Multiple Pheromone Types and Other Extensions to the Ant-Miner Classification Rule Discovery Algorithm.”, Swarm Intelligence. Springer, pp. 182-196. doi: 10.1007/s11721-011-0057-9.
Silla Jr, C. N. and Freitas, A. A. (2011) “Selecting different protein representations and classification algorithms in hierarchical protein function prediction”, Intelligent Data Analysis. IOS Press, pp. 182-196. doi: 10.3233/IDA-2011-0505.
Cerri, R., de Carvalho, A. C. and Freitas, A. A. (2011) “Adapting non-hierarchical multilabel classification methods for hierarchical multilabel classification”, Intelligent Data Analysis. IOS Press, pp. 182-196. Available at: http://www.cs.kent.ac.uk/pubs/2011/3191.
Pereira, R. B., Plastino, A., Zadrozny, B., Merschmann, L. H. de C. and Freitas, A. A. (2011) “Improving lazy attribute selection”, Journal of Information and Data Management. Sociedade Brasileira de Computacao, pp. 182-196. Available at: http://www.cs.kent.ac.uk/pubs/2011/3170.
Tsunoda, D. F., Freitas, A. A. and Lopes, H. S. (2011) “A genetic programming method for protein motif discovery and protein classification”, Soft Computing. Springer, pp. 182-196. doi: 10.1007/s00500-010-0624-9.
Pereira, R. B., Plastino, A., Zadrozny, B., Merschmann, L. H. de C. and Freitas, A. A. (2011) “Lazy Attribute selection: Choosing attributes at classification time.”, Intelligent Data Analysis. IOS Press, pp. 182-196. Available at: http://www.cs.kent.ac.uk/pubs/2011/3158.
Davies, M. N., Gloriam, D. E., Secker, A. D., Freitas, A. A., Timmis, J. and Flower, D. R. (2011) “Present perspectives on the automated classification of the G-Protein Coupled Receptors (GPCRs) at the Protein Sequence Level”, Current Topics in Medicinal Chemistry. Bentham Science Publishers, pp. 182-196. Available at: http://www.cs.kent.ac.uk/pubs/2011/3169.
Freitas, A. A. and de Magalhaes, J. P. (2011) “A review and appraisal of the DNA damage theory of ageing”, Mutation Research. Elsevier, pp. 182-196. Available at: http://www.cs.kent.ac.uk/pubs/2011/3148.
Plastino, A., Fuchshuber, R., Martins, S. de L., Freitas, A. A. and Salhi, S. (2011) “A hybrid data mining metaheuristic for the p-median problem”, Statistical Analysis & Data Mining Journal. Wiley Periodicals, pp. 313-335. doi: 10.1002/sam.10116.
Freitas, A. A., Vasieva, O. and de Magalhaes, J. P. (2011) “A data mining approach for classifying DNA repair genes into ageing-related or non-ageing-related”, BMC Genomics, pp. 182-196. doi: 10.1186/1471-2164-12-27.
Silla Jr, C. N. and Freitas, A. A. (2011) “A survey of hierarchical classification across different application domains”, Data Mining and Knowledge Discovery, pp. 182-196. doi: 10.1007/978-3-642-01799-5â??.
Otero, F. E., Freitas, A. A. and Johnson, C. G. (2010) “A hierarchical multi-label classification ant colony algorithm for protein function prediction”, Memetic Computing. Springer, pp. 165-181. doi: 10.1007/s12293-010-0045-4.
La Torre, A., Pena, J., Muelas, S. and Freitas, A. A. (2010) “Learning hybridization strategies in evolutionary algorithms”, Intelligent Data Analysis. IOS Press, pp. 182-196. doi: 10.3233/IDA-2010-0424.
Freitas, A. A., Wieser, D. C. and Apweiler, R. (2010) “On the importance of comprehensible classification models for protein function prediction”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, pp. 182-196. doi: 10.1109/TCBB.2008.47.
Secker, A. D., Davies, M. N., Freitas, A. A., Clark, E., Timmis, J. and Flower, D. R. (2010) “Hierarchical classification of G-Protein-Coupled Receptors with data-driven selection of attributes and classifiers”, International Journal of Data Mining and Bioinformatics, pp. 182-196. Available at: http://www.cs.kent.ac.uk/pubs/2010/3111.
Pappa, G. L. and Freitas, A. A. (2009) “Evolving rule induction algorithms with multi-objective grammar-based genetic programming”, Knowledge and Information Systems. Springer, pp. 283-309. doi: 10.1007/s10115-008-0171-1.
Secker, A. D., Davies, M. N., Freitas, A. A., Timmis, J., Clark, E. and Flower, D. R. (2009) “An artificial immune system for clustering amino acids in the context of protein function classification”, Journal of Mathematical Modelling and Algorithms. Springer, pp. 103-123. doi: 10.1007/s10852-009-9107-3.
Pappa, G. L. and Freitas, A. A. (2009) “Automatically evolving rule induction algorithms tailored to the prediction of postsynaptic activity in proteins”, Intelligent Data Analysis. IOS Press, pp. 243-259. doi: 10.3233/IDA-2009-0366.
Hruschka, E. R., Campello, R. J. G. B., Freitas, A. A. and de Carvalho, A. C. (2009) “A survey of evolutionary algorithms for clustering”, IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews. IEEE Press, pp. 133-155. doi: 10.1109/TSMCC.2008.2007252.
Holden, N. and Freitas, A. A. (2009) “Hierarchical classification of protein function with ensembles of rules and particle swarm optimisation”, Soft Computing. Springer Berlin, pp. 259-272. doi: 10.1007/s00500-008-0321-0.
Basgalupp, M. P., de Carvalho, A. C., Barros, R. C., Ruiz, D. D. and Freitas, A. A. (2009) “Lexicographic multi-objective evolutionary induction of decision trees”, International Journal of Bio-Inspired Computation, pp. 105-117. doi: 10.1504/IJBIC.2009.022779.
Davies, M. N., Secker, A. D., Freitas, A. A., Timmis, J., Clark, E. and Flower, D. R. (2008) “Alignment-independent techniques for protein classification”, Current Proteomics, pp. 217-223.
Ghosh, S., Froebrich, D. and Freitas, A. A. (2008) “Robust autonomous detection of the defective pixels in detectors using a probabilistic technique”, Applied Optics. Optics Society of America, pp. 6904-6924.
Iqbal, M., Freitas, A. A., Johnson, C. G. and Vergassola, M. (2008) “Message-passing algorithms for the prediction of protein domain interactions from protein?protein interaction data”, Bioinformatics. Oxford University Press, pp. 2064-2070. doi: 10.1093/bioinformatics/btn366.
Davies, M. N., Secker, A. D., Freitas, A. A., Clark, E., Timmis, J. and Flower, D. R. (2008) “Optimizing amino acid groupings for GPCR classification”, Bioinformatics. Oxford University Press, England, pp. 1980-1986. doi: 10.1093/bioinformatics/btn382.
Davies, M. N., Secker, A. D., Halling-Brown, M., Moss, D. S., Freitas, A. A., Timmis, J., Clark, E. and Flower, D. R. (2008) “GPCRTree: online hierarchical classification of GPCR function”, BMC Research Notes, p. 5 pages. doi: 10.1186/1756-0500-1-67.
Poli, R., Kennedy, J., Blackwell, T. and Freitas, A. A. (2008) “Special Issue Editorial: Particle Swarms: the Second Decade”, Journal of Artificial Evolution and Applications. Hindawi Publishing Corporation, p. 3 pages. doi: 10.1155/2008/108972.
Correa, E. S., Freitas, A. A. and Johnson, C. G. (2008) “Particle swarm for attribute selection in Bayesian classification: an application to protein function prediction”, Journal of Artificial Evolution and Applications. Hindawi Publishing Corporation, p. 12 pages. doi: 10.1155/2008/876746.
Holden, N. and Freitas, A. A. (2008) “A hybrid PSO/ACO algorithm for discovering classification rules in data mining”, Journal of Artificial Evolution and Applications. Hindawi Publishing Corporation, p. 11 pages. doi: 10.1155/2008/316145.
Secker, A. D., Freitas, A. A. and Timmis, J. (2008) “AISIID: An artificial immune system for interesting information discovery on the web”, Applied Soft Computing. Elsevier Science, pp. 885-905. doi: 10.1016/j.asoc.2007.07.007.
Davies, M. N., Secker, A. D., Freitas, A. A., Mendao, M., Timmis, J. and Flower, D. R. (2007) “On the hierarchical classification of G Protein-Coupled Receptors”, Bioinformatics. Oxford University Press, pp. 3113-3118. doi: 10.1093/bioinformatics/btm506.
Secker, A. D., Davies, M. N., Freitas, A. A., Timmis, J., Mendao, M. and Flower, D. R. (2007) “An experimental comparison of classification algorithms for hierarchical prediction of protein function”, Expert Update (Magazine of the British Computer Society’s Specialist Group on AI), pp. 17-22.
Freitas, A. A. and Timmis, J. (2007) “Revisiting the Foundations of Artificial Immune Systems for Data Mining”, IEEE Transactions on Evolutionary Computation. IEEE Press, pp. 521-540. doi: 10.1109/TEVC.2006.884042.
Davies, M. N., Gloriam, D. E., Secker, A. D., Freitas, A. A., Mendao, M., Timmis, J. and Flower, D. R. (2007) “Proteomic applications of automated GPCR classification”, Proteomics. Wiley-VHC Verlag, pp. 2800-2814. doi: 10.1002/pmic.200700093.
Freitas, A. A. (2006) “Are we really discovering ’’interesting’’ knowledge from data?”, Expert Update (the BCS-SGAI Magazine). The British Computer Society, pp. 41-47.
Pappa, G. L., Baines, A. J. and Freitas, A. A. (2005) “Predicting Post-Synaptic Activity in Proteins with Data Mining”, Bioinformatics. Oxford University Press, pp. ii19-ii25. doi: 10.1093/bioinformatics/bti1102.
Carvalho, D. R. and Freitas, A. A. (2005) “Evaluating six candidate solutions for the small-disjunct problem and choosing the best solution via meta-learning”, Artificial Intelligence Review. Springer, pp. 61-98.
Freitas, A. A. (2004) “A critical review of multi-objective optimization in data mining: a position paper”, SIGKDD Explorations. ACM Press, pp. 77-86. doi: 10.1145/1046456.1046467.
Carvalho, D. R. and Freitas, A. A. (2004) “A hybrid decision tree/genetic algorithm method for data mining”, Information Sciences. Elsevier, pp. 13-35. doi: 10.1016/j.ins.2003.03.013.
Romao, W., Freitas, A. A. and Gimenes, I. M. de S. (2004) “Discovering interesting knowledge from a science & technology database with a genetic algorithm”, Applied Soft Computing. Elsevier, pp. 121-137. doi: 10.1016/j.asoc.2003.10.002.
Correa, E. S., Steiner, M. T. A., Freitas, A. A. and Carnieri, C. (2004) “A genetic algorithm for solving a capacitated p-median problem”, Numerical Algorithms. Kluwer, pp. 373-388. doi: 10.1023/B:NUMA.0000021767.42899.31.
Bojarczuk, C. C., Lopes, H. S., Freitas, A. A. and Michalkiewicz, E. L. (2004) “A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets”, Artificial Intelligence in Medicine. Elsevier, pp. 27-48. doi: 10.1016/j.artmed.2003.06.001.
Ghosh, A. and Freitas, A. A. (2003) “Guest Editorial: Data Mining and Knowledge Discovery with Evolutionary Algorithms”, IEEE Transactions on Evolutionary Computation. IEEE Press, pp. 517-518. doi: 10.1109/TEVC.2003.819653.
Carvalho, D. R. and Freitas, A. A. (2002) “A genetic algorithm for discovering small disjunct rules in data mining”, Applied Soft Computing. Elsevier, pp. 75-88. doi: 10.1016/S1568-4946(02)00031-5.
Parpinelli, R. S., Lopes, H. S. and Freitas, A. A. (2002) “Data Mining with an Ant Colony Optimization Algorithm”, IEEE Transactions on Evolutionary Computation. IEEE Press, pp. 321-332. doi: 10.1109/TEVC.2002.802452.
Freitas, A. A. (2001) “Understanding the crucial role of attribute interaction in data mining”, Artificial Intelligence Review. Kluwer, pp. 177-199. doi: 10.1023/A:1011996210207.
Romao, W., Freitas, A. A. and Pacheco, R. (2000) “Uma revisao de abordagens geneticodifusas para descoberta de conhecimento em banco de dados”, Acta Scientiarum. Universidade Estadual de Maringa, Brazil, pp. 1347-1359.
Book section
Cagnini, H. E. L., Freitas, A. A. and Barros, R. C. (2020) “An Evolutionary Algorithm for Learning Interpretable Ensembles of Classifiers”, in Cerri, R. and Prati, R. C. (eds.) Intelligent Systems. 9th Brazilian Conference, BRACIS 2020, Rio Grande, Brazil, October 20–23, 2020, Proceedings, Part I. Springer, pp. 18-33. doi: 10.1007/978-3-030-61377-8_2.
de Sá, A. G. C., Pimenta, C. G., Pappa, G. L. and Freitas, A. A. (2020) “A robust experimental evaluation of automated multi-label classification methods”, in GECCO ’20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference. 2020 Genetic and Evolutionary Computation Conference (GECCO’20), New York, USA: ACM, pp. 175-183. doi: 10.1145/3377930.3390231.
Fabris, F. and Freitas, A. A. (2020) “Analysing the overfit of the auto-sklearn automated machine learning tool.”, in Machine Learning, Optimization, and Data Science 5th International Conference. 5th International Conference on Machine Learning, Optimization and Data Science (LOD 2019), Cham, Switzerland: Springer, pp. 508-520. doi: 10.1007/978-3-030-37599-7_42.
de Sá, A. G. C., Freitas, A. A. and Pappa, G. L. (2018) “Automated Selection and Configuration of Multi-Label Classification Algorithms with Grammar-Based Genetic Programming”, in Auger, A., Fonseca, C. M., Lourenço, N., Machado, P., Paquete, L., and Whitley, D. (eds.) Parallel Problem Solving from Nature – PPSN XV. PPSN: 15th International Conference on Parallel Problem Solving from Nature, Springer, pp. 308-320. doi: 10.1007/978-3-319-99259-4_25.
de Sá, A. G., Pappa, G. L. and Freitas, A. A. (2017) “Towards a method for automatically selecting and configuring multi-label classification algorithms”, in Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO 2017 - 7th Workshop on Evolutionary Computation for the Automated Design of Algorithms, New York, USA: ACM, pp. 1125-1132. doi: 10.1145/3067695.3082053.
Cramer, S., Kampouridis, M., Freitas, A. A. and Alexandridis, A. (2016) “Predicting Rainfall in the Context of Rainfall Derivatives Using Genetic Programming”, in 2015 IEEE Symposium Series on Computational Intelligence. IEEE Computational Intelligence for Financial Engineering & Economics, Symposium Series on Computational Intelligence, IEEE, pp. 711-718. doi: 10.1109/SSCI.2015.108.
Fabris, F. and Freitas, A. A. (2015) “A novel extended hierarchical dependence network based on non-hierarchical predictive classes and applications to ageing-related data”, in 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI). 2015 IEEE 27th International Conference on Tools with Artificial Intelligence, IEEE, pp. 711-718. doi: 10.1109/ICTAI.2015.53.
Jungjit, S. and Freitas, A. A. (2015) “A new genetic algorithm for multi-label correlation-based feature selection.”, in ESANN 2015 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. The 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN, pp. 285-290.
Jungjit, S. and Freitas, A. A. (2015) “A lexicographic multi-objective genetic algorithm for multi-label correlation-based feature selection”, in Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation. Workshop on Evolutionary Rule Learning at the 2015 Genetic and Evolutionary Computation Conference (GECCO-2015), New York, USA: ACM, pp. 989-996. doi: 10.1145/2739482.2768448.
Gonçalves, E. C., Plastino, A. and Freitas, A. A. (2015) “Simpler is better: a novel genetic algorithm to induce compact multi-label chain classifiers”, in Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation. 2015 Conference on Genetic and Evolutionary Computation Conference (GECCO-2015), New York, USA: ACM, pp. 559-566. doi: 10.1145/2739480.2754650.
Wan, C. and Freitas, A. A. (2015) “Two methods for constructing a gene ontology-based feature network for a Bayesian network classifier and applications to datasets of aging-related genes.”, in Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics. The 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics (ACM-BCB 2015)., New York, USA: ACM, pp. 27-36. doi: 10.1145/2808719.2808722.
Fabris, F. and Freitas, A. A. (2014) “Dependency network methods for hierarchical multi-label classification of gene functions”, in 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE, pp. 241-248. doi: 10.1109/CIDM.2014.7008674.
Freitas, A. A. (2014) “An Efficient Algorithm for Hierarchical Classification of Protein and Gene Functions”, in 2014 25th International Workshop on Database and Expert Systems Applications. Twenty-Fifth International Workshop on Database and Expert System Applications (DEXA 2014), IEEE, pp. 64-68. doi: 10.1109/DEXA.2014.29.
Nascimento da Silva, P., Corrêa Gonçalves, E., Plastino, A. and Freitas, A. A. (2014) “Distinct chains for different instances: an effective strategy for multi-label classifier chains”, in Machine Learning and Knowledge Discovery in Databases European Conference. ECML PKDD: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Berlin, Germany: Springer, pp. 453-468. doi: 10.1007/978-3-662-44851-9_29.
Freitas, A. A. (2013) “Prediction of the Pro-longevity or Anti-Longevity Effect of Caenorhabditis Elegans Genes Based on Bayesian Classification Methods”, in 2013 IEEE International Conference on Bioinformatics and Biomedicine. 2013 IEEE International Conference on Bioinformatics and Medicine, IEEE, pp. 373-380. doi: 10.1109/BIBM.2013.6732521.
Goncalves, E. C., Plastino, A. and Freitas, A. A. (2013) “A Genetic Algorithm for Optimizing the Label Ordering in Multi-Label Classifier Chains”, in 2013 IEEE 25th International Conference on Tools with Artificial Intelligence. IEEE 25th International Conference on Tools with Artificial Intelligence, IEEE, pp. 469-476. doi: 10.1109/ICTAI.2013.76.
de Campos Merschmann, L. H. and Freitas, A. A. (2013) “An Extended Local Hierarchical Classifier for Prediction of Protein and Gene Functions.”, in Bellatreche, L. and Mohania, M. K. (eds.) Data Warehousing and Knowledge Discovery 15th International Conference. Data Warehousing and Knowledge Discovery, 15th International Conference (DaWaK 2013), Berlin, Germany: Springer, pp. 159-171. doi: 10.1007/978-3-642-40131-2_14.
Salama, K. M. and Freitas, A. A. (2013) “Extending the ABC-Miner Bayesian classification algorithm.”, in Nature Inspired Cooperative Strategies for Optimization (NICSO 2013) Learning, Optimization and Interdisciplinary Applications. International Workshop on Natural Inspired Cooperative Strategies for Optimization (NICSO 2013), Cham, Switzerland: Springer, pp. 1-12. doi: 10.1007/978-3-319-01692-4_1.
Barros, R. C., Cerri, R., Freitas, A. A. and de Carvalho, A. C. (2013) “Probabilistic clustering for hierarchical multi-label classification of protein functions”, in Machine Learning and Knowledge Discovery in Databases European Conference. Machine Learning and Knowledge Discovery in Databases: European Conference (ECMLPKDD-2013), Berlin, Germany: Springer, pp. 385-400. doi: 10.1007/978-3-642-40991-2_25.
Salama, K. M. and Freitas, A. A. (2013) “Investigating the Impact of Various Classification Quality Measures in the Predictive Accuracy of ABC-Miner”, in 2013 IEEE Congress on Evolutionary Computation. 2013 IEEE Congress on Evolutionary Computation (CEC-2013), IEEE, pp. 2321-2328. doi: 10.1109/CEC.2013.6557846.
Salama, K. M. and Freitas, A. A. (2013) “Clustering-based Bayesian Multi-net Classifier Construction with Ant Colony Optimization”, in 2013 IEEE Congress on Evolutionary Computation. 2013 IEEE Congress on Evolutionary Computation (CEC-2013), IEEE, pp. 3079-3086. doi: 10.1109/CEC.2013.6557945.
Cerri, R., Barros, R. C., de Carvalho, A. C. and Freitas, A. A. (2013) “A grammatical evolution algorithm for the generation of hierarchical multi-label classification rules”, in 2013 IEEE Congress on Evolutionary Computation. 2013 IEEE Congress on Evolutionary Computation (CEC-2013), IEEE, pp. 454-461. doi: 10.1109/CEC.2013.6557604.
Salama, K. M. and Freitas, A. A. (2013) “ACO-based Bayesian network ensembles for the hierarchical classification of ageing-related proteins”, in Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics 11th European Conference. 11th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Berlin, Germany: Springer, pp. 80-91. doi: 10.1007/978-3-642-37189-9_8.
Salama, K. M. and Freitas, A. A. (2012) “ABC-Miner: an ant-based Bayesian classification algorithm.”, in Swarm Intelligence 8th International Conference. Swarm Intelligence: 8th International Conference (ANTS 2012)., Berlin, Germany: Springer, pp. 13-24. doi: 10.1007/978-3-642-32650-9_2.
Basgalupp, M. P., Barros, R. C., de Carvalho, A. C. and Freitas, A. A. (2012) “A beam search based decision tree induction algorithm”, in Kulkarni, S. (ed.). IGI Global, pp. 357-370. doi: 10.4018/978-1-4666-1833-6.ch020.
Barros, R. C., Basgalupp, M. P., de Carvalho, A. C. and Freitas, A. A. (2012) “A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms”, in Moore, J. H., Soule, T., Banzhaf, W., Llora, X., Auger, A., Ritchie, M., Ochoa, G., Rand, B., Bongard, J., Loiacono, D., Mehnen, J., and Davis, D. (eds.) Proceedings of the 14th annual conference on Genetic and evolutionary computation. Proceedings of 2012 Genetic and Evolutionary Computation Conference (GECCO), New York, USA: ACM, pp. 1237-1244. doi: 10.1145/2330163.2330335.
Jungjit, S., Freitas, A. A., Michaelis, M. and Cinatl, J. (2012) “A multi-label correlation-based feature selection method for the classification of neuroblastoma microarray data.”, in Bichindaritz, I., Perner, P., Ruß, G., and Schmidt, R. (eds.) Advances in Data Mining: 12 Industrial Conference ICDM 2012 Workshop Proceedings. Advances in Data Mining: 12th Industrial Conference (ICDM 2012) Workshop Proceedings Â? Workshop on Data Mining in Life Sciences (DMLS 2012)., IBAI Publishing, pp. 149-157. Available at: http://www.cs.kent.ac.uk/pubs/2012/3253.
Xavier, J. C., Canuto, A. M., Freitas, A. A., Gonçalves, L. M. and Silla Jr, C. N. (2011) “A hierarchical approach to represent relational data applied to clustering tasks”, in The 2011 International Joint Conference on Neural Networks. Proceedings of the 2011 International Joint Conference on Neural Networks, IEEE, pp. 182-196. doi: 10.1109/IJCNN.2011.6033624.
Barros, R. C., Basgalupp, M. P., de Carvalho, A. C. and Freitas, A. A. (2011) “Towards the automatic design of decision tree induction algorithms”, in Proceedings of the 13th annual conference companion on Genetic and evolutionary computation. Proceedings of the GECCO-2011 First Workshop on Evolutionary Algorithms for Evolving Generic Algorithms, New York, USA: ACM, pp. 182-196. doi: 10.1145/2001858.2002050.
Keysermann, M., Freitas, A. and Vargas, P. (2011) “Implementing a data mining approach to episodic memory modelling for artificial companions”, in Kazakov, D. and Tsoulas, G. (eds.) Human Memory for Artificial Agents: AISB 2011 Symposium. Proceedings of AISBÂ?11: Human Memory for Artificial Agents, Society for the Study of Artificial Intelligence and the Simulation of Behaviour, pp. 182-196.
Pappa, G. L. and Freitas, A. A. (2010) “Creating rule ensembles from automatically-evolved rule induction algorithms”, in Advances in Machine Learning I: Dedicated to the memory of Prof. Ryszard S. Michalski. Springer, pp. 182-196. doi: 10.1007/978-3-642-05177-7.
Xavier, J. C., Freitas, A. A., Canuto, A. M. and Gonçalves, L. M. (2010) “Web log data clustering for a multi-agent recommendation system”, in 2010 International Conference on Machine Learning and Cybernetics. Machine Learning and Cybernetics (ICMLC): 2010 International Conference on, IEEE, pp. 182-196. doi: 10.1109/ICMLC.2010.5581017.
Alves, R. T., Delgado, M. and Freitas, A. A. (2010) “Knowledge discovery with artificial immune systems for hierarchical multi-label classification of protein functions”, in Sobrevilla, P., Aranda, J., and Xambo, S. (eds.) International Conference on Fuzzy Systems. 2010 World Congress on Computational Intelligence (WCII/FUZZ-IEEE 2010), IEEE, pp. 182-196. doi: 10.1109/FUZZY.2010.5584298.
Barros, R. C., Basgalupp, M. P., Ruiz, D. D., de Carvalho, A. C. and Freitas, A. A. (2010) “Evolutionary model tree induction”, in Shin, D. (ed.) SAC ’10 Proceedings of the 2010 ACM Symposium on Applied Computing. Applied Computing 2010: Proc. 25th Annual ACM Symposium on Applied Computing (SAC-2010), New York, USA: ACM, pp. 182-196. doi: 10.1145/1774088.1774327.
Tsunoda, D. F., Freitas, A. A. and Lopes, H. S. (2009) “A Genetic Programming-Based Tool for Protein Classification”, in Abraham, A., Benitez, J., Herrera, F., Loia, V., Marcelloni, F., and Senatore, S. (eds.) 2009 Ninth International Conference on Intelligent Systems Design and Applications. Proc. 9th Int. Conf. on Intelligent System Design and Applications (ISDAÂ?09), IEEE, pp. 182-196. doi: 10.1109/ISDA.2009.14.
Silla Jr, C. N. and Freitas, A. A. (2009) “A global-model naive Bayes approach to the hierarchical prediction of protein functions”, in Wang, W., Kargupta, H., Ranka, S., Yu, P. S., and Wu, X. (eds.) 2009 Ninth IEEE International Conference on Data Mining. Proc. Ninth IEEE Int. Conf. on Data Mining (ICDM-2009), IEEE, pp. 182-196. doi: 10.1109/ICDM.2009.85.
Tsunoda, D. F., Lopes, H. S. and Freitas, A. A. (2009) “A hybrid evolutionary approach for the protein classification problem”, in Nguyen, N. T., Kowalczyk, R., and Chen, S.-M. (eds.) Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems First International Conference. Proc. 1st Int. Conf. on Collective Intelligence ( ICCI 2009), Berlin, Germany: Springer, pp. 182-196. doi: 10.1007/978-3-642-04441-0_55.
Silla Jr, C. N. and Freitas, A. A. (2009) “Novel top-down approaches for hierarchical classification and their application to automatic music genre classification”, in Chen, C. and Roberts, R. (eds.) 2009 IEEE International Conference on Systems, Man and Cybernetics. Proc. 2009 IEEE Int. Conf. on Systems, Man and Cybernetics (SMC-2009), IEEE, pp. 182-196. doi: 10.1109/ICSMC.2009.5346776.
de Carvalho, A. C. and Freitas, A. A. (2009) “A tutorial on multi-label classification techniques”, in Abraham, A., Hassanien, A.-E., and Snasel, V. (eds.) Foundations of Computational Intelligence. Springer, pp. 177-195.
Iqbal, M., Freitas, A. A. and Johnson, C. G. (2009) “A hybrid rule-induction/likelihood-ratio based approach for predicting protein-protein interactions”, in Mumford, C. L. and Jain, L. C. (eds.) Computational Intelligence: Collaboration, Fusion and Emergence. Springer, pp. 623-637. doi: 10.1007/978-3-642-01799-519.
Otero, F. E., Segond, M., Freitas, A. A., Johnson, C. G., Robilliard, D. and Fonlupt, C. (2009) “An empirical evaluation of the effectiveness of different types of predictor attributes in protein function prediction.”, in Abraham, A., Hassanien, A.-E., and Snasel, V. (eds.) Studies in Computational Intelligence. Berlin: Springer, pp. 339-357. doi: 10.1007/978-3-642-01536-6_13.
Basgalupp, M. P., Barros, R. C., de Carvalho, A. C., Freitas, A. A. and Ruiz, D. D. (2009) “LEGAL-Tree: a lexicographic multi-objective genetic algorithm for decision tree induction”, in Shin, S., Ossowski, S., Martins, P., Menezes, R., Virol, M., Hong, J., Shin, D., Palakal, M., Fritzke, U., Crosby, M., and Haddad, H. (eds.) SAC ’09 Proceedings of the 2009 ACM symposium on Applied Computing. Proceedings of the 2009 ACM Symposium on Applied Computing, New York, USA: ACM, pp. 1085-1090. doi: 10.1145/1529282.1529521.
Otero, F. E., Freitas, A. A. and Johnson, C. G. (2009) “A hierarchical classification ant colony algorithm for predicting gene ontology terms”, in Pizzuti, C., Ritchie, M., and Giacobini, M. (eds.) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics 7th European Conference. Proc. 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBio-2009), Berlin, Germany: Springer, pp. 68-79. doi: 10.1007/978-3-642-01184-9_7.
Bevilacqua, V., Menolascina, F., Alves, R. T., Tommasi, S., Mastronardi, G., Delgado, M., Paradiso, A., Nicosia, G. and Freitas, A. A. (2008) “Artificial Immune Systems in Bioinformatics”, in Smolinski, T., Milanova, M. G., and Hassanien, A.-E. (eds.) Computational Intelligence in Biomedicine and Bioinformatics: current trends and applications. Berlin: Springer, pp. 271-296. doi: 10.1007/978-3-540-70778-3_113.
Otero, F. E., Freitas, A. A. and Johnson, C. G. (2008) “cAnt-Miner: an ant colony classification algorithm to cope with continuous attributes”, in Dorigo, M. (ed.) Ant Colony Optimization and Swarm Intelligence 6th International Conference. Ant Colony Optimization and Swarm Intelligence (Proc. ANTS 2008), LNCS 5217, Berlin, Germany: Springer, pp. 48-59. doi: 10.1007/978-3-540-87527-7_5.
Costa, E. P., Lorena, A. C., Carvalho, A. C. P. L. F. and Freitas, A. A. (2008) “Top-down hierarchical ensembles of classifiers for predicting G-Protein-Coupled-Receptor functions”, in Bazzan, A. L., Craven, M., and Martins, N. F. (eds.) Advances in Bioinformatics and Computational Biology Third Brazilian Symposium on Bioinformatics. Advances in Bioinformatics and Computational Biology (Proc. 2008 Brazilian Symposium in Bioinformatics (BSB-2008)), Lecture Notes in Bioinformatics 5167, Berlin, Germany: Springer, pp. 35-46. doi: 10.1007/978-3-540-85557-6_4.
Alves, R. T., Delgado, M. and Freitas, A. A. (2008) “Multi-label hierarchical classification of protein functions with artificial immune systems”, in Bazzan, A. L., Craven, M., and Martins, N. F. (eds.) Advances in Bioinformatics and Computational Biology Third Brazilian Symposium on Bioinformatics. Advances in Bioinformatics and Computational Biology (Proc. 2008 Brazilian Symposium in Bioinformatics (BSB-2008)), Lecture Notes in Bioinformatics 5167, Berlin, Germany: Springer, pp. 1-12. doi: 10.1007/978-3-540-85557-6_1.
Secker, A. D., Davies, M. N., Freitas, A. A., Timmis, J., Clark, E. and Flower, D. R. (2008) “An artificial immune system for evolving amino acid clusters tailored to protein function prediction”, in Bentley, P. J., Lee, D., and Jung, S. (eds.) Artificial Immune Systems. 7th International Conference on Artificial Immune Systems, Springer, pp. 242-253.
Correa, E. S., Freitas, A. A. and Johnson, C. G. (2008) “A New Discrete Particle Swarm Algorithm Applied to Attribute Selection in a Bioinformatics Data Set”, in Proceedings of the 8th annual conference on Genetic and evolutionary computation. 8th annual conference on Genetic and evolutionary computation, New York, USA: ACM, pp. 35-42. doi: 10.1145/1143997.1144003.
Ghosh, S., Marshall, I. W. and Freitas, A. A. (2008) “Autonomously detecting the defective pixels in an imaging sensor array using a robust statistical technique”, in Image Quality and System Performance V. Image Quality and Systems Performance V ? Proc. of SPIE-IS&T Electronic Imaging, SPIE Vol. 6808, Bellingham, Washington: SPIE. doi: 10.1117/12.765147.
Holden, N. and Freitas, A. A. (2008) “Improving the performance of hierarchical classification with swarm intelligence”, in Marchiori, E. and Moore, J. H. (eds.) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics 6th European Conference. 6th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Berlin, Germany: Springer, pp. 48-60. doi: 10.1007/978-3-540-78757-0_5.
Iqbal, M., Freitas, A. A. and Johnson, C. G. (2008) “Protein interaction inference using particle swarm optimization algorithm”, in Marchiori, E. and Moore, J. H. (eds.) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics 6th European Conference. 6th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Berlin, Germany: Springer, pp. 61-70. doi: 10.1007/978-3-540-78757-0_6.
Ghosh, S., Freitas, A. and Marshall, I. (2007) “Robust Autonomous Detection of the Faulty Sensors of a Sensor Array”, in 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2007, CAMPSAP 2007, IEEE, pp. 233-236. doi: 10.1109/CAMSAP.2007.4498008.
Pappa, G. L. and Freitas, A. A. (2007) “Discovering new rule induction algorithms with grammar-based genetic programming”, in Maimon, O. and Rokach, L. (eds.) Soft Computing for Knowledge Discovery and Data Mining. New York: Springer, pp. 133-152. doi: 10.1007/978-0-387-69935-6_6.
Freitas, A. A. (2007) “A review of evolutionary algorithms for data mining”, in Maimon, O. and Rokach, L. (eds.) Soft Computing for Knowledge Discovery and Data Mining. Springer, pp. 61-93.
Secker, A. D. and Freitas, A. A. (2007) “WAIRS: Improving Classification Accuracy by Weighting Attributes in the AIRS Classifier”, in 2007 IEEE Congress on Evolutionary Computation. Proceedings of the 2007 IEEE Congress on Evolutionary Computation (CEC 2007), IEEE, pp. 3759-3765. doi: 10.1109/CEC.2007.4424960.
Costa, E. P., Lorena, A. C., Carvalho, A. C. P. L. F., Freitas, A. A. and Holden, N. (2007) “Comparing several approaches for hierarchical classification of proteins with decision trees”, in Sagot, M.-F. and Walter, M. E. M. T. (eds.) Advances in Bioinformatics and Computational Biology: Second Brazilian Symposium on Bioinformatics. Advances in Bioinformatics and Computational Biology (Proc. of the Second Brazilian Symposium on Bioinformatics, BSB-2007), Lecture Notes in Bioinformatics 4643, Berlin, Germany: Springer, pp. 126-137. doi: 10.1007/978-3-540-73731-5_12.
Costa, E. P., Lorena, A. C., Carvalho, A. C. and Freitas, A. A. (2007) “A review of performance evaluation measures for hierarchical classifiers”, in Drummond, C., Elazmeh, W., Japkowicz, N., and Macskassy, S. (eds.) Proceedings of the 2007 AAAI Workshop Evaluation Methods for Machine Learning II. Evaluation Methods for Machine Learning II: papers from the AAAI-2007 Workshop, AAAI Press, pp. 1-6.
Holden, N. and Freitas, A. A. (2007) “A hybrid PSO/ACO algorithm for classification”, in Yu, T. (ed.) GECCO ’07 Proceedings of the 9th annual conference companion on Genetic and evolutionary computation. Proc. of the GECCO-2007 Workshop on Particle Swarms: The Second Decade, New York, USA: ACM, pp. 2745-2750. doi: 10.1145/1274000.1274080.
Correa, E. S., Freitas, A. A. and Johnson, C. G. (2007) “Particle swarm and bayesian networks applied to attribute selection for protein functional classification”, in Yu, T. (ed.) GECCO ’07 Proceedings of the 9th annual conference companion on Genetic and evolutionary computation. Proc. of the GECCO-2007 Workshop on Particle Swarms: The Second Decade, New York, USA: ACM, pp. 2651-2658. doi: 10.1145/1274000.1274081.
Freitas, A. A. and de Carvalho, A. C. (2007) “A Tutorial on Hierarchical Classification with Applications in Bioinformatics.”, in Taniar, D. (ed.) Research and Trends in Data Mining Technologies and Applications. USA: IGI Publishing, pp. 175-208.
Freitas, A. A., McGarry, K. and Correa, E. S. (2007) “Integrating Bayesian networks and Simpson’s paradox in data mining”, in Russo, F. and Williamson, J. (eds.) Causality and Probability in the Sciences. United Kingdom: College Publications, pp. 43-62.
Miles, N., Freitas, A. A. and Serjeant, S. (2006) “Estimating photometric redshifts using genetic algorithms”, in Ellis, R., Allen, T., and Tuson, A. (eds.) Applications and Innovations in Intelligent Systems XIV Proceedings of AI-2006, the Twenty-sixth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence. Applications and innovations in intelligent systems XIV - Proc. of AI-2006, New York, USA: Springer, pp. 75-87. doi: 10.1007/978-1-84628-666-7_6.
McGarry, K., Morris, N. and Freitas, A. A. (2006) “The integration of heterogeneous biological data using Bayesian networks”, in Ellis, R., Allen, T., and Tuson, A. (eds.) Applications and Innovations in Intelligent Systems XIV Proceedings of AI-2006, the Twenty-sixth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence. Applications and innovations in intelligent systems XIV - Proc. of AI-2006, Springer, pp. 44-57. doi: 10.1007/978-1-84628-666-7_4.
Pappa, G. L. and Freitas, A. A. (2006) “Automatically Evolving Rule Induction Algorithms”, in Fuernkranz, J., Scheffer, T., and Spiliopoulou, M. (eds.) Machine Learning: ECML 2006 17th European Conference on Machine Learning. Proc. of the 17th European Conference on Machine Learning, Berlin: Springer, pp. 341-352. doi: 10.1007/11871842_34.
Chan, A. and Freitas, A. A. (2006) “A New Ant Colony Algorithm for Multi-Label Classification with Applications in Bioinformatics”, in Keijzer, M. (ed.) Proceedings of the 8th annual conference on Genetic and evolutionary computation. 2006 Genetic and Evolutionary Computation Conference, New York, USA: ACM, pp. 27-34. doi: 10.1145/1143997.1144002.
Smaldon, J. and Freitas, A. A. (2006) “A New Version of the Ant-Miner Algorithm Discovering Unordered Rule Sets”, in Keijzer, M. (ed.) Proceedings of the 8th annual conference on Genetic and evolutionary computation. 2006 Genetic and Evolutionary Computation Conference, New York, USA: ACM, pp. 43-50. doi: 10.1145/1143997.1144004.
Noda, E. and Freitas, A. A. (2006) “Discovering knowledge nuggets with a genetic algorithm”, in Triantaphyllou, E. and Felici, G. (eds.) Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques. New York, New York (USA): Springer, pp. 395-432. doi: 10.1007/0-387-34296-6_12.
Chan, A. and Freitas, A. A. (2006) “A New Classification-Rule Pruning Procedure for an Ant Colony Algorithm”, in Talbi, E.-G., Liardet, P., Collet, P., Lutton, E., and Schoenauer, M. (eds.) Artificial Evolution 7th International Conference. Artificial Evolution: Proc. 7th Int. Conf. (EA-2005, Lille, France, Oct. 2005), Berlin, Germany: Springer, pp. 25-36. doi: 10.1007/11740698_3.
Carvalho, D. R., Freitas, A. A. and Ebecken, N. (2005) “Evaluating the correlation between objective rule interestingness measures and real human interest”, in Jorge, A., Torgo, L., Brazdil, P., Camacho, R., and Gama, J. (eds.) Knowledge Discovery in Databases: PKDD 2005 9th European Conference on Principles and Practice of Knowledge Discovery in Databases. Knowledge Discovery in Databases: Proc. of PKDD-2005. LNAI 3731, Berlin, Germany: Springer, pp. 453-461. doi: 10.1007/11564126_45.
Polack, F. A., Stepney, S., Turner, H., Welch, P. H. and Barnes, F. R. (2005) “An Architecture for Modelling Emergence in CA-Like Systems”, in Capcarrere, M. S., Freitas, A. A., Bentley, P. J., Johnson, C. G., and Timmis, J. (eds.) Advances in Artificial Life 8th European Conference. Advances in Artificial Life, 8th European Conference on Artificial Life (ECAL 2005), Berlin, Germany: Springer, pp. 427-436. doi: 10.1007/11553090_44.
Chu, D. and Rowe, J. (2005) “A Fitness-Landscape for the Evolution of Uptake Signal Sequences on Bacterial DNA”, in Capcarrere, M. S., Freitas, A. A., Bentley, P. J., Johnson, C. G., and Timmis, J. (eds.) Advances in Artificial Life 8th European Conference. Advances in Artificial Life: 8th European Conference, ECAL 2005, Berlin, Germany: Springer, pp. 845-853. doi: 10.1007/11553090_85.
Holden, N. and Freitas, A. A. (2005) “A hybrid particle swarm/ant colony algorithm for the classification of hierarchical biological data”, in Arabshahi, P. and Martinoli, A. (eds.) Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. 2005 IEEE Swarm Intelligence Symposium, IEEE, pp. 100-107. doi: 10.1109/SIS.2005.1501608.
Tsunoda, D. F., Lopes, H. S. and Freitas, A. A. (2005) “An evolutionary approach for motif discovery and transmembrane protein classification”, in Rothlauf, F. (ed.) Applications of Evolutionary Computing EvoWorkkshops 2005: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, and EvoSTOC. Applications of Evolutionary Computing (Proc. of EvoBIO-2005: 3rd European Workshop on Evolutionary Bioinformatics), LNCS 3449, Berlin, Germany: Springer, pp. 105-114. doi: 10.1007/978-3-540-32003-6_11.
Parpinelli, R. S., Lopes, H. S. and Freitas, A. A. (2005) “Classification-Rule Discovery with an Ant Colony Algorithm.”, in Khosrow-Pour, M. (ed.) Encyclopedia of Information Science and Technology. Hershey: Idea Group, pp. 420-424.
Secker, A. D., Freitas, A. A. and Timmis, J. (2005) “Towards a danger theory inspired artificial immune system for web mining”, in Scime, A. (ed.) Web Mining: applications and techniques. Idea Group, pp. 145-168.
Freitas, A. A. (2005) “Evolutionary Algorithms for Data Mining”, in Maimon, O. and Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook. Springer, pp. 435-467. doi: 10.1007/b107408.
Goncalves, A., Freitas, A. A., Kato, R. and de Oliveira, R. (2005) “Using genetic algorithms to mine interesting dependence modeling rules”, in Hamza, M. (ed.) Databases and Applications. Proc. 23rd IASTED Int. Multi-Conference on Databases and Applications (DBA-2005), ACTA Press, pp. 1-6.
Pappa, G. L., Freitas, A. A. and Kaestner, C. A. (2004) “Multi-Objective Algorithms for Attribute Selection in Data Mining”, in Coello, C. C. and Lamont, G. (eds.) Applications of Multi-Objective Evolutionary Algorithms. World Scientific, pp. 603-626.
Alves, R. T., Delgado, M., Lopes, H. S. and Freitas, A. A. (2004) “Induction of fuzzy classification rules with an artificial immune system”, in Barros, A., Araujo, A., Yehia, H., and Teixeira, R. (eds.) Proceedings of the 8th Brazillian Symposium on Neural Networks. Proc. 8th Brazilian Symp. on Neural Networks, IEEE.
Silla Jr, C. N., Pappa, G. L., Freitas, A. A. and Kaestner, C. A. (2004) “Automatic Text Summarization with Genetic Algorithm-Based Attribute Selection”, in Lemaitre, C., Reyes, C. A., and Gonzalez, J. A. (eds.) Advances in Artificial Intelligence – IBERAMIA 2004 9th Ibero-American Conference on AI. 9th Ibero-American Conference on Artificial Intelligence (IBERAMIA 2004), Berlin, Germany: Springer, pp. 305-314. doi: 10.1007/978-3-540-30498-2_31.
Holden, N. and Freitas, A. A. (2004) “Web page classification with an ant colony algorithm”, in Parallel Problem Solving from Nature - PPSN VIII 8th International Conference. Parallel Problem Solving from Nature - PPSN VIII, LNCS 3242, Berlin, Germany: Springer, pp. 1092-1102. doi: 10.1007/978-3-540-30217-9_110.
Alves, R. T., Delgado, M., Lopes, H. S. and Freitas, A. A. (2004) “An artificial immune system for fuzzy-rule induction in data mining”, in Yao, X. (ed.) Parallel Problem Solving from Nature - PPSN VIII 8th International Conference. Parallel Problem Solving from Nature - PPSN VIII, LNCS 3242, Berlin, Germany: Springer, pp. 1011-1020. doi: 10.1007/978-3-540-30217-9_102.
Ferreira, S. N., Freitas, A. A. and Avila, B. C. (2004) “Handling inconsistency in distributed data mining with paraconsistent logic”, in Guzelis, C., Alpaydin, E., Yakhno, T., and Gurgen, F. (eds.) Proceedings of the Thirteenth Turkish Symposium on Artificial Intelligence and Neural Networks. Proc. 13th Turkish Symposium on Artificial Intelligence and Neural Networks (TAINN-2004), pp. 19-28.
Secker, A. D., Freitas, A. A. and Timmis, J. (2003) “AISEC: An Artificial Immune System for E-mail Classification”, in Sarker, R. A., Reynolds, R., Abbass, H. A., Kay-Chen, T., McKay, R., Essam, D., and Gedeon, T. (eds.) The 2003 Congress on Evolutionary Computation. Proceedings of the Congress on Evolutionary Computation, IEEE, pp. 131-139. doi: 10.1109/CEC.2003.1299566.
Carvalho, D. R., Freitas, A. A. and Ebecken, N. (2003) “A critical review of rule surprisingness measures”, in Ebecken, N., Brebbia, C., and Zanasi, A. (eds.) Data Mining. Proc. Data Mining IV - Int. Conf. on Data Mining, WIT Press, pp. 545-556. doi: 10.2495/DATA030531.
Parpinelli, R. S., Lopes, H. S. and Freitas, A. A. (2003) “Mining comprehensible rules from data with an ant colony algorithm”, in Bittencourt, G. and Ramalho, G. L. (eds.) Advances in Artificial Intelligence 16th Brazilian Symposium on Artificial Intelligence. Proc. 16th Brazilian Symp. on Artificial Intelligence (SBIA-2002). Lecture Notes in Artificial Intelligence 2507, Berlin, Germany: Springer, pp. 259-269. doi: 10.1007/3-540-36127-8_25.
Neto, J. L., Freitas, A. A. and Kaestner, C. A. (2003) “Automatic text summarization using a machine learning approach”, in Bittencourt, G. and Ramalho, G. L. (eds.) Advances in Artificial Intelligence 16th Brazilian Symposium on Artificial Intelligence. Proc. 16th Brazilian Symp. on Artificial Intelligence (SBIA-2002). Lecture Notes in Artificial Intelligence 2507, Springer, pp. 205-215. doi: 10.1007/3-540-36127-8_20.
Otero, F. E., Silva, M. M., Freitas, A. A. and Nievola, J. C. (2003) “Genetic Programming for Attribute Construction in Data Mining”, in Ryan, C., Keijzer, M., Poli, R., Soule, T., Tsang, E., and Costa, E. (eds.) Genetic Programming 6th European Conference. Genetic Programming: Proc. 6th European Conference (EuroGP-2003)., Berlin, Germany: Springer, pp. 384-393. doi: 10.1007/3-540-36599-0_36.
Bojarczuk, C. C., Lopes, H. S. and Freitas, A. A. (2003) “An innovative application of a constrained-syntax genetic programming system to the problem of predicting survival of patients”, in Ryan, C., Keijzer, M., Poli, R., Soule, T., Tsang, E., and Costa, E. (eds.) Genetic Programming 6th European Conference. Genetic Programming: Proc. 6th European Conference (EuroGP-2003), Berlin, Germany: Springer, pp. 11-21. doi: 10.1007/3-540-36599-0_2.
Freitas, A. A. (2003) “A survey of evolutionary algorithms for data mining and knowledge discovery”, in Ghosh, A. and Tsutsui, S. (eds.) Advances in Evolutionary Computation. Berli: Springer-Verlag, pp. 819-845. Available at: http://www.cs.kent.ac.uk/pubs/2002/1582.
Noda, E., Freitas, A. A. and Yamakami, A. (2002) “A Distributed-Population GA for Discovering Interesting Prediction Rules”, in Benitez, J. and Gordon, O. (eds.) Advances in Soft Computing: Engineering Design and Manufacturing. 7th Online World Conference on Soft Computing in Industrial Applications (WSC7), London: Springer, pp. 287-296. doi: 10.1007/978-1-4471-3744-3_28.
Pappa, G. L., Freitas, A. A. and Kaestner, C. A. (2002) “A multiobjective genetic algorithm for attribute selection”, in Lofti, A., Garibaldi, J., and John, R. (eds.) Proceedings Of The 4th International Conference On Recent Advances In Soft Computing. Proc. 4th Int. Conf. on Recent Advances in Soft Computing (RASC-2002), Nottingham Trent University, pp. 116-121.
Parpinelli, R. S., Lopes, H. S. and Freitas, A. A. (2002) “An Ant Colony Algorithm for Classification Rule Discovery”, in Abbass, H. A., Sarker, R. A., and Newton, C. S. (eds.) Data Mining: a Heurstic Approach. London: Idea Group Publishing, pp. 191-208.
Freitas, A. A. (2002) “A Review of Evolutionary Algorithms for E-Commerce”, in Segovia, J., Szczepaniak, P., and Niedzwiedzinski, M. (eds.) E-Commerce and Intelligent Methods. Studies in Fuzziness and Soft Computing. Heidelberg, Berlin: Springer-Verlag, pp. 159-179.
Otero, F. E., Silva, M. M. and Freitas, A. A. (2002) “Genetic Programming for Attribute Construction in Data Mining”, in Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation. Proc Genetic and Evolutionary Computation Conf (GECCO-2002), San Francisco, California, USA: Morgan Kaufmann, pp. 1270-1270.
Freitas, A. A. (2002) “Evolutionary Computation”, in Klosgen, W. and Zytkow, J. (eds.) Handbook of Data Mining and Knowledge Discovery. New York: Oxford University Press.
Romao, W., Freitas, A. A. and Pacheco-Lopez, R. (2002) “A Genetic Algorithm for Discovering Interesting Fuzzy Prediction Rules: applications to science and technology data”, in Langdon, W. B. and Cantu-Paz, E. (eds.) Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation. Proceedings of Genetic and Evolutionary Computation Conference (GECCO-2002), San Francisco, California, USA: Morgan Kaufmann, pp. 1188-1195.
Carvalho, D. R. and Freitas, A. A. (2002) “A genetic algorithm with sequential niching for discovering small-disjunct rules”, in Langdon, W. B. and Cantu-Paz, E. (eds.) Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation. Proceedings Genetic and Evolutionary Computation Conference (GECCO-2002), San Francisco, California, USA: Morgan Kaufmann, pp. 1035-1042.
Larsen, O., Freitas, A. A. and Nievola, J. C. (2002) “Constructing X-of-N Attributes with a Genetic Algorithm”, in Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation. Proc Genetic and Evolutionary Computation Conf (GECCO-2002), San Francisco, California, USA: Morgan Kaufmann.
Carvalho, D. R. and Freitas, A. A. (2001) “An Immunological Algorithm for Discovering Small-disjunct Rules in Data Mining”, in Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation. Proc Graduate Student Workshop at GECCO-2001, San Francisco, California, USA: Morgan Kaufmann, pp. 401-404.
Parpinelli, R. S., Lopes, H. S. and Freitas, A. A. (2001) “An ant colony based system for data mining: applications to medical data”, in Spector, L. E. and Goodman, E. D. (eds.) Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation. Proc. 2001 Genetic and Evolutionary Computation Conference (GECCO-2001), San Francisco, California, USA: Morgan Kaufmann, pp. 791-798.
Santos Correa, E., Steiner, M. T. A., Freitas, A. A. and Carnieri, C. (2001) “A Genetic Algorithm for the P-median Problem”, in Spector, L. E. and Goodman, E. D. (eds.) Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation. Proc. 2001 Genetic and Evolutionary Computation Conference (GECCO-2001), San Francisco, California, USA: Morgan Kaufmann, pp. 1268-1275.
Mendes, R., Voznika, F., Freitas, A. A. and Nievola, J. C. (2001) “Discovering fuzzy classification rules with genetic programming and co-evolution”, in Principles of Data Mining and Knowledge Discovery 5th European Conference. Principles of Data Mining and Knowledge Discovery (Proc. 5th European Conference PKDD 2001) - Lecture Notes in Artificial Intelligence, Berlin, Germany: Springer, pp. 314-325. doi: 10.1007/3-540-44794-6_26.
Conference or workshop item
Ribeiro, C. and Freitas, A. (2021) “Constructed temporal features for longitudinal classification of human ageing data”, in. 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), IEEE, pp. 106-112. doi: 10.1109/ICHI52183.2021.00027.
Maia, M., Plastino, A. and Freitas, A. A. (2021) “An ensemble of naïve Bayes classifiers for uncertain categorical data”, in. 21st IEEE International Conference on Data Mining (ICDM 2021), Los Alamitos, CA, USA: IEEE Computer Society – Conference Publishing Services, pp. 1216-1221. doi: 10.1109/ICDM51629.2021.00148.
Pomsuwan, T. and Freitas, A. A. (2020) “Adapting random forests to cope with heavily censored datasets in survival analysis”, in. 2020 European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020), dblp, pp. 697-702. Available at: https://www.esann.org/sites/default/files/proceedings/2020/ES2020-117.pdf.
Ribeiro, C. and Freitas, A. A. (2020) “A New Random Forest Method for Longitudinal Data Classification Using a Lexicographic Bi-Objective Approach”, in. 2020 IEEE Symposium Series on Computational Intelligence (SSCI 2020), IEEE.
Wan, C. and Freitas, A. A. (2020) “Hierarchical Dependency Constrained Averaged One-Dependence Estimators Classifiers for Hierarchical Feature Spaces.”, in. 10th International Conference on Probabilistic Graphic Models (PGM 2020), pp. 557-568. doi: http://proceedings.mlr.press/v138/wan20a.html.
Ovchinnik, S., Otero, F. E. and Freitas, A. A. (2019) “Monotonicity Detection and Enforcement in Longitudinal Classification”, in Bramer, M. and Petridis, M. (eds.). 39th SGAI International Conference on Artificial Intelligence, AI 2019, Springer. doi: 10.1007/978-3-030-34885-4_5.
Freitas, A. A. (2019) “Automated machine learning for studying the trade-off between predictive accuracy and interpretability”, in. Third IFIP International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE 2019), Springer, pp. 48-66. doi: 10.1007/978-3-030-29726-8_4.
Miranda, E. S., Fabris, F., Nascimento, C. G. M., Freitas, A. A. and Oliveira, A. C. M. (2018) “Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem”, in. 2018 7th Brazilian Conference on Intelligent Systems (BRACIS), USA: IEEE, pp. 169-174. doi: 10.1109/BRACIS.2018.00037.
Xavier-Junior, J. C., Freitas, A. A., Feitosa-Neto, A. and Ludermir, T. B. (2018) “A Novel Evolutionary Algorithm for Automated Machine Learning Focusing on Classifier Ensembles”, in. 2018 7th Brazilian Conference on Intelligent Systems (BRACIS), USA: IEEE, pp. 462-467. doi: 10.1109/BRACIS.2018.00086.
Freitas, A. A., Pinto Junior, J. A., Plastino, A. and Neumann, N. M. (2018) “Is P-value<0.05 Enough? Two Case Studies in Classifiers Evaluation”, in. XV National Meeting of Artificial and Computational Intelligence (ENIAC), pp. 94-103. doi: 10.5753/eniac.2018.4407.
Goncalves, E., Freitas, A. A. and Plastino, A. (2018) “A survey of genetic algorithms for multi-label classification”, in. 2018 IEEE Congress on Evolutionary Computation (CEC 2018), New York, NY, USA: IEEE, pp. 981-988. doi: 10.1109/CEC.2018.8477927.
da Silva, P., Plastino, A. and Freitas, A. A. (2018) “A Novel Genetic Algorithm for Feature Selection in Hierarchical Feature Spaces”, in. SIAM International Conference on Data Mining (SDM18), pp. 738-746. doi: 10.1137/1.9781611975321.83.
Pomsuwan, T. and Freitas, A. A. (2017) “Feature Selection for the Classification of Longitudinal Human Ageing Data”, in. Data Mining Workshops (ICDMW), 2017 IEEE International Conference, USA: IEEE, pp. 739-746. doi: 10.1109/ICDMW.2017.102.
Martire, I., da Silva, P., Plastino, A., Fabris, F. and Freitas, A. A. (2017) “A novel probabilistic Jaccard distance measure for classification of sparse and uncertain data”, in Rebeiro de Faria Paiva, E., Merschmann, L., and Cerri, R. (eds.). 5th Brazilian Symposium on Knowledge Discovery, Mining and Learning (KDMiLe), pp. 81-88. Available at: http://www.facom.ufu.br/~kdmile/proceedings/anais-kdmile-2017.pdf.
Cramer, S., Kampouridis, M., Freitas, A. A. and Alexandridis, A. (2017) “Pricing Rainfall Based Futures Using Genetic Programming”, in. 20th European Conference, EvoApplications: European Conference on the Applications of Evolutionary Computation, Springer, pp. 17-33. doi: 10.1007%2F978-3-319-55849-3_2.
Cramer, S., Kampouridis, M. and Freitas, A. A. (2016) “Feature Engineering for Improving Financial Derivatives-based Rainfall Prediction”, in. IEEE World Congress on Evolutionary Computation.
Cramer, S., Kampouridis, M. and Freitas, A. A. (2016) “A Genetic Decomposition Algorithm for Predicting Rainfall within Financial Weather Derivatives”, in. Genetic and Evolutionary Computation Conference (GECCO 2016).
Jungjit, S., Freitas, A. A., Michaelis, M. and Cinatl, J. (2014) “Extending multi-label feature selection with KEGG pathway information for microarray data analysis”, in. 2014 IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology.
Jungjit, S., Freitas, A. A., Michaelis, M. and Cinatl, J. (2013) “Two extensions to multi-lable correlation-based feature selection: a case study in bioinformatics.”, in. 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC-2013), IEEE Press, pp. 1519-1524.
Otero, F. E. and Freitas, A. A. (2013) “Improving the interpretability of classification rules discovered by an ant colony algorithm.”, in. 2013 Genetic and Evolutionary Computation Conference (GECCO’13), New York, NY, USA.: ACM Press, pp. 73-80.
Medland, M., Otero, F. E. and Freitas, A. A. (2012) “Improving the cAnt-MinerPB Classification Algorithm”, in Dorigo, M., Birattari, M., Blum, C., Christensen, A. L., Engelbrecht, A. P., Groß, R., and Stützle, T. (eds.). 8th International Conference on Swarm Intelligence (ANTS 2012), Springer Berlin Heidelberg, pp. 73-84. doi: 10.1007/978-3-642-32650-9.
Moraglio, A., Otero, F. E., Johnson, C. G., Thompson, S. and Freitas, A. A. (2012) “Evolving Recursive Programs using Non-recursive Scaffolding”, in. Proceedings of the 2012 IEEE World Congress on Computational Intelligence, pp. 1596-1603. Available at: http://www.cs.kent.ac.uk/pubs/2012/3225.
Meiguins, A. S. G., Limao, R., Meiguins, B. S., Junior, S. and Freitas, A. A. (2012) “AutoClustering: an estimation of distribution algorithm for the automatic generation of clustering algorithms.”, in. Proceedings of WCCI 2012 Â? IEEE World Congress on Computational Intelligence (Congress on Evolutionary Computation), IEEE Press, pp. 2560-2566. Available at: http://www.cs.kent.ac.uk/pubs/2012/3249.
Otero, F. E., Johnson, C. G., Freitas, A. A. and Thompson, S. (2010) “Refactoring in Automatically Generated Programs”, in. 2nd International Symposium on Search Based Software Engineering.
Otero, F. E., Freitas, A. A. and Johnson, C. G. (2009) “Handling continuous attributes in ant colony classification algorithms”, in. Proc. of the 2009 IEEE Symposium on Computational Intelligence in Data Mining (CIDM 2009), IEEE, pp. 225-231. doi: 10.1109/CIDM.2009.4938653.
Salhi, S., Plastino, A., Fonseca, E. R., Martins, S. de L., Freitas, A. A., Luis, M. and Fuchshuber, R. (2009) “Hybrid data mining metaheuristic for the p median problem”, in. SIAM conference.
de Oliveira, C. S., Meiguins, A. S. G., Meiguins, B. S., Godinho, P. I. and Freitas, A. A. (2007) “An evolutionary density and grid-based clustering algorithm.”, in daSilva, A., Soares, V., and Elias, G. (eds.). Proc. of the XXIII Brazilian Symposium on Databases (SBBD-2007), Sociedade Brasileira de Computacao, pp. 175-189.
Alves, R. T., Delgado, M., Camargo, F., Benelli, E. and Freitas, A. A. (2007) “Discovering multi-label hierarchical classification rules for protein function prediction”, in Plastino, A., de Carvalho, A. C., Ramos, R., and Junior, W. (eds.). Proc. II Workshop em Algoritmos e Aplicacoes de Mineracao de Dados (Workshop on Algorithms and Applications of Data Mining), Sociedade Brasileira de Computacao, pp. 87-90.
Smaldon, J. and Freitas, A. A. (2006) “Improving the interpretability of classification rules in sparse bioinformatics datasets”, in Bramer, M., Coenen, F., and Tuson, A. (eds.). Research and Development in Intelligent Systems XXIII - Proc. AI-2006, New York: Springer-Verlag, pp. 377-381.
Holden, N. and Freitas, A. A. (2006) “Hierarchical classification of G-protein-coupled receptors with a PSO/ACO algorithm”, in. IEEE Swarm Intelligence Symposium 2006, IEEE Press, pp. 77-84.
Iqbal, M., Freitas, A. A. and Johnson, C. G. (2005) “Varying the Topology and Probability of Re-Initialization in Particle Swarm Optimization”, in Talbi, E.-G. (ed.). Evolution Artificielle 2005.
Pappa, G. L. and Freitas, A. A. (2004) “Towards a genetic programming algorithm for automatically evolving rule induction algorithms”, in Furnkranz, J. (ed.). Proc. ECML/PKDD-2004 Workshop on Advances in Inductive Learning, Pisa, Italy, pp. 93-108.
Silla Jr, C. N., Kaestner, C. A. and Freitas, A. A. (2003) “A non-linear topic detection method for text summarization using Wordnet”, in Nunes, M. da G. V., Aluisio, S., Oliveira, L., and Teles, J. (eds.). Proc. I Workshop em Tecnologia da Informacao e Linguagem Humana, ICMC-USP, Brazil. Available at: http://www.cs.kent.ac.uk/pubs/2003/1761.
Freitas, A. A. and Timmis, J. (2003) “Revisiting the Foundations of Artificial Immune Systems: A Problem Oriented Perspective”, in Timmis, J., Bentley, P. J., and Hart, E. (eds.). Proceedings of the 2nd International Conference on Artificial Immune Systems, Springer, pp. 229-241. Available at: http://www.cs.kent.ac.uk/pubs/2003/1693.
Secker, A. D., Freitas, A. A. and Timmis, J. (2003) “A Danger Theory Approach to Web Mining”, in Timmis, J., Bentley, P. J., and Hart, E. (eds.). Proceedings of the 2nd International Conference on Artificial Immune Systems, Springer, pp. 156-167. doi: 10.1007/b12020.
Larsen, O., Freitas, A. A. and Nievola, J. C. (2002) “Constructing X-of-N attributes with a genetic algorithm”, in Lofti, A., Garibaldi, J., and John, R. (eds.). Proc. 4th Int. Conf. on Recent Advances in Soft Computing (RASC-2002), Nottingham Trent University, pp. 326-331.
Carvalho, D. R. and Freitas, A. A. (2002) “New results for a hybrid decision tree/genetic algorithm for data mining”, in Lofti, A., Garibaldi, J., and John, R. (eds.). Proc. 4th Int. Conf. on Recent Advances in Soft Computing (RASC-2002), Berlin: Springer, pp. 260-265.
Pappa, G. L., Freitas, A. A. and Kaestner, C. A. (2002) “Attribute Selection with a Multiobjective Genetic Algorithm”, in Bittencourt, G. and Ramalho, G. L. (eds.). Proc. 16th Brazilian Symp. on Artificial Intelligence (SBIA-2002), Berlin: Springer-Verlag, pp. 280-290. doi: 10.1007/3-540-36127-8_27.
Fabris, C. C. and Freitas, A. A. (2001) “Incorporating deviation-detection functionality into the OLAP paradigm”, in Mattoso, M. and Xexeo, G. (eds.). Proc. XVI Brazilian Symposium on Databases (SBBD-2001), Rio de Janeiro, Brazil, pp. 274-285.
Bojarczuk, C. C., Lopes, H. S. and Freitas, A. A. (2001) “Data Mining with Constrained-syntax Genetic Programming: Applications in Medical Data Sets”, in. Proc Intelligent Data Analysis in Medicine and Pharmacology - a workshop at MedInfo-2001, London.
Book
Pappa, G. L. and Freitas, A. A. (2010) Automating the design of data mining algorithms: an evolutionary computation approach. Springer, pp. 182-196. Available at: http://www.cs.kent.ac.uk/pubs/2010/2999.
Freitas, A. A. (2002) Data Mining and Knowledge Discovery with Evolutionary Algorithms. Berlin: Spinger-Verlag.
Datasets / databases
Newby, D., Freitas, A. A. and Ghafourian, T. (2014) “A Compilation of Aqueous Solubility of Drugs and Drug-Like Compounds.”
Other
Ghosh, A. and Freitas, A. A. (2003) “Special Issue on data mining and knowledge discovery with evolutionary algorithms”. IEEE Trans. on Evolutionary Computation 7(6), pp. 517-575. Available at: http://www.cs.kent.ac.uk/pubs/2003/1767.
Edited book
Capcarrere, M. S., Freitas, A. A., Bentley, P. J., Johnson, C. G. and Timmis, J. (eds.) (2005) Advances in Artificial Life 8th European Conference, ECAL 2005, Canterbury, UK, September 5-9, 2005. Proceedings, Advances in Artificial Life: 8th European Conference, ECAL 2005, Canterbury, UK, September 5-9, 2005, Proceedings (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence). 8th European Conference, ECAL 2005, Berlin, Germany: Springer. doi: 10.1007/11553090.
Review
Freitas, A. A. (2001) “Book Review: Data Mining Using Grammar-based Genetic Programming and Applications”, Genetic Programming and Evolvable Machines. Kluwer, pp. 197-199. doi: 10.1023/A:1011564616547.
Total publications in KAR: 250 [See all in KAR]

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Last Updated: 05/12/2021