Publications by Alex A. Freitas

          Books

  • G.L.Pappa and A.A. Freitas. Automating the Design of Data Mining Algorithms: an Evolutionary Computation Approach. Springer, 2010. xiii + 187 pages. Publisher's webpage about the book


  • A.A. Freitas. Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer, 2002. xiv + 264 pages. Book Cover   Table of Contents


  • A.A. Freitas and S.H. Lavington. Mining Very Large Databases with Parallel Processing. Kluwer, 1998. ix + 208 pages. Table of Contents and Publisher's address


    •           Book Chapters

  • M. Iqbal, A.A. Freitas, C.G. Johnson. A hybrid rule-induction/likelihood-ratio based approach for predicting protein-protein interactions. In: C.L. Mumford and L.C. Jain (Eds.) Computational Intelligence: collaboration, fusion and emergence, pp. 623-637. Springer, 2009. (pre-print version) (pdf)

  • F. Otero, M. Segond, A.A. Freitas, C.G. Johnson, D. Robilliard, C. Fonlupt. An empirical evaluation of the effectiveness of different types of predictor attributes in protein function prediction. In: A. Abraham, A.-E. Hassanien, V. Snael (Eds.) Foundations of Computational Intelligence, Vol 5, Studies in Computational Intelligence 205, pp. 339-357. Springer, 2009. (pre-print version) (pdf)

  • A.A. Freitas, R.S. Parpinelli, H.S. Lopes. Ant Colony Algorithms for Data Classification. In: M. Khosrou-Pour (Ed.) Encyclopedia of Information Science and Technology, 2nd Ed, pp. 154-159. Information Science Reference, 2008. (pre-print version) (pdf)

  • A.A. Freitas. A Review of Evolutionary Algorithms for Data Mining. In: O. Maimon and L. Rokach (Eds.) Soft Computing for Knowledge Discovery and Data Mining, pp. 61-93. Springer, 2007. (pre-print version) (pdf)

  • A.A. Freitas and A.C.P.L.F. de Carvalho. A Tutorial on Hierarchical Classification with Applications in Bioinformatics. In: D. Taniar (Ed.) Research and Trends in Data Mining Technologies and Applications, pp. 175-208. Idea Group, 2007. (pre-print, unformatted version) (pdf)

  • A.A. Freitas, K. McGarry and E.S. Correa. Integrating Bayesian networks and Simpson's paradox in data mining. In: F. Russo and J. Williamson (Eds.) Causality and Probability in the Sciences, pp. 43-62. London: College Publications, 2007. (pre-print, unformatted version) (pdf)

  • G.L. Pappa, A.A. Freitas and C.A.A. Kaestner. Multi-Objective Algorithms for Attribute Selection in Data Mining. In: C.A. Coello Coello and G.B. Lamont (Eds.) Applications of Multi-Objective Evolutionary Algorithms, pp. 603-626. World Scientific, 2004. (pre-print, unformatted version) (pdf)

  • A. Secker, A.A. Freitas, J. Timmis. Towards a Danger Theory Inspired Artificial Immune System for Web Mining. In: A. Scime (Ed.) Web Mining: applications and techniques, pp. 145-168. Idea Group, 2005. (pre-print, unformatted version) (pdf)

  • A.A. Freitas. A Review of Evolutionary Algorithms for E-Commerce. In: J. Segovia, P.S. Szczepaniak, M. Niedzwiedzinski (Eds.) E-Commerce and Intelligent Methods. Studies in Fuzziness and Soft Computing, Vol. 105, pp. 159-179. Heidelberg: Springer-Verlag, 2002. (pre-print, unformatted version) (postscript) (pdf)

  • A.A. Freitas. Evolutionary Computation. W. Klosgen and J. Zytkow (Eds.) Handbook of Data Mining and Knowledge Discovery, pp. 698-706. Oxford University Press, 2002. (pre-print, unformatted version) (postscript) (pdf)

  • R.S. Parpinelli, H.S. Lopes and A.A. Freitas. An Ant Colony Algorithm for Classification Rule Discovery. In: H. Abbass, R. Sarker, C. Newton. (Eds.) Data Mining: a Heuristic Approach, pp. 191-208. London: Idea Group Publishing, 2002. (pre-print, unformatted version) (pdf)

              Journal/Magazine Papers

  • G.L. Pappa and A.A. Freitas. Automatically evolving rule induction algorithms tailored to the prediction of postsynaptic activity in proteins. Intelligent Data Analysis, Vol. 13, No. 2, 2009, pp. 243-259. (pre-print, unformatted version) (pdf)

  • G.L. Pappa and A.A. Freitas. Evolving rule induction algorithms with multi-objective grammar-based genetic programming. Knowledge and Information Systems, Vol. 19, No. 3, June 2009, pp. 283-309 (pre-print, unformatted version) (pdf)

  • E.R. Hruschka, R.J.G.B. Campello, A.A. Freitas and A.C.P.L.F. de Carvalho. A survey of evolutionary algorithms for clustering. IEEE Transactions on Systems, Man and Cybernetics - Part C: Applications and Reviews. Vol. 39, No. 2, March 2009, pp. 133-155. (pre-print version) (pdf)

  • N. Holden and A.A. Freitas. Hierarchical classification of protein function with ensembles of rules and particle swarm optimisation. Soft Computing journal, Vol. 13, No. 3, Feb. 2009, pp. 259-272. (pre-print version) (pdf) (the datasets used in the experiments are available from here)

  • M.N. Davies, A. Secker, A.A. Freitas, J. Timmis, E. Clark, D.R. Flower. Alignment-independent techniques for protein classification. Current Proteomics, Vol. 5, No. 4, Dec. 2008, pp. 217-223. (pre-print, unformatted version) (pdf)

  • M.N. Davies, A. Secker, A.A. Freitas, E. Clark, J. Timmis, D.R. Flower. Optimizing amino acid groupings for GPCR classification. Bioinformatics Vol. 24, No. 18, 2008, pp. 1980-1986. (pre-print version) (pdf)

  • M. Iqbal, A.A. Freitas, C.G. Johnson, M. Vergassola. Message-passing algorithms for the prediction of protein domain interactions from protein-protein interaction data. Bioinformatics Vol. 24, No. 18, 2008, pp. 2064-2070. (pre-print version) (pdf)

  • N. Holden and A.A. Freitas. A hybrid PSO/ACO algorithm for discovering classification rules in data mining. Journal of Artificial Evolution and Applications (JAEA), special issue on Particle Swarms: The Second Decade, Vol. 2008, Article Id 316145, 11 pages. (pdf)

  • E.S. Correa, A.A. Freitas and C.G. Johnson. Particle swarm for attribute selection in Bayesian classification: an application to protein function prediction. Journal of Artificial Evolution and Applications (JAEA), special issue on Particle Swarms: The Second Decade, Vol. 2008, Article Id 876746, 12 pages. (pdf)

  • A. Secker, A.A. Freitas and J. Timmis. AISIID: an artificial immune system for interesting information discovery on the web. Applied Soft Computing 8 (2008), pp. 885-905. (pdf)

  • M.N. Davies, A. Secker, A.A. Freitas, M. Mendao, J. Timmis and D.R. Flower. On the hierarchical classification of G protein-coupled-receptors. Bioinformatics 2007, Vol. 23, No. 23, 1 December 2007, pp. 3113-3118. (pre-print version) (pdf)

  • M.N. Davies, D.E. Gloriam, A. Secker, A.A. Freitas, M. Mendao, J. Timmis and D.R. Flower. Proteomics applications of automated GPCR classification. Proteomics 7, 2007, pp. 2800-2814. (pdf)

  • A.A. Freitas and J. Timmis. Revisiting the Foundations of Artificial Immune Systems for Data Mining. IEEE Trans. on Evolutionary Computation, Vol. 11, Issue 4, pp. 521-540, Aug. 2007. (pre-print, unformatted version) (pdf)

  • A. Secker, M.N. Davies, A.A. Freitas, J. Timmis, M. Mendao, D. Flower. An experimental comparison of classification algorithms for the hierarchical prediction of protein function. Expert Update (the BCS-SGAI Magazine), Vol. 9, No. 3, Special Issue on the 3rd UK KDD Workshop, pp. 17-22, Autumn 2007. (pdf)

  • A.A. Freitas. Are we really discovering "interesting" knowledge from data? Expert Update (the BCS-SGAI Magazine), Vol. 9, No. 1, Special Issue on the 2nd UK KDD Workshop, pp. 41-47, Autumn 2006. (pre-print, unformatted version) (pdf)

  • C.C. Fabris and A.A. Freitas. Discovering surprising instances of Simpson's paradox in hierarchical multidimensional data. Int. Journal of Data Warehousing and Mining, 2(1), pp. 26-48, Jan-Mar 2006. (pre-print version) (pdf)

  • G.L. Pappa, A.J. Baines and A.A. Freitas. Predicting post-synaptic activity in proteins with data mining. Bioinformatics Vol. 21 Suppl. 2, 2005, pp. ii19-ii25. (pre-print version) (pdf), (dataset used in the experiments)

  • D.R. Carvalho and A.A. Freitas. Evaluating Six Candidate Solutions for the Small-Disjunct Problem and Choosing the Best Solution via Meta Learning. Artificial Intelligence Review, 24(1), pp. 61-98, Sep. 2005. (pre-print version) (pdf)

  • A.A. Freitas. A Critical Review of Multi-Objective Optimization in Data Mining: a position paper. ACM SIGKDD Explorations, 6(2), pp. 77-86, 2004. (pre-print version) (pdf)

  • D.R. Carvalho and A.A. Freitas. A hybrid decision tree/genetic algorithm method for data mining. Information Sciences 163(1-3), pp. 13-35. June 2004. (pre-print, unformatted version) (pdf)

  • W. Romao, A.A. Freitas, I.M.S. Gimenes. Discovering Interesting Knowledge from a Science & Technology Database with a Genetic Algorithm. Applied Soft Computing 4(2004), pp. 121-137. (pre-print, unformatted version) (pdf)

  • C.C. Bojarczuk, H.S. Lopes, A.A. Freitas, E.L. Michalkiewicz. A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets. AI in Medicine 30(2004), pp. 27-48. (pre-print, unformatted version) (pdf)

  • D.R. Carvalho and A.A. Freitas. A genetic algorithm for discovering small disjunct rules in data mining. Applied Soft Computing, 2(2), pp. 75-88, Dec. 2002. (pre-print, unformatted version) (pdf)

  • R.S. Parpinelli, H.S. Lopes and A.A. Freitas. Data Mining with an Ant Colony Optimization Algorithm. IEEE Trans. on Evolutionary Computation, special issue on Ant Colony algorithms, 6(4), pp. 321-332, Aug. 2002. (pre-print, unformatted version) (pdf)

  • A.A. Freitas. Understanding the Crucial Role of Attribute Interaction in Data Mining. Artificial Intelligence Review 16(3), Nov. 2001, pp. 177-199. (pre-print, unformatted version) (postscript) (pdf)

  • A.A. Freitas. Book Review: Data mining using grammar-based genetic programming and applications. Genetic Programming and Evolvable Machines, 2(2), 197-199. June 2001. (pre-print, unformatted version) (postscript)

  • C.C. Bojarczuk, H.S. Lopes, A.A. Freitas. Genetic programming for knowledge discovery in chest pain diagnosis. IEEE Engineering in Medicine and Biology magazine - special issue on data mining and knowledge discovery, 19(4), 38-44, July/Aug. 2000. (pre-print, unformatted version) (postscript) (pdf)

  • W. Romao, A.A. Freitas and R.S. Pacheco. Uma revisao de abordagens genetico-difusas para descoberta de conhecimento em banco de dados. (In Portuguese) Acta Scientiarum 22(5), 1347-1359. Dec. 2000. Universidade Estadual de Maringa, Brazil. (pre-print, unformatted version) (postscript) (pdf)

  • A.A. Freitas. Understanding the crucial differences between classification and discovery of association rules - a position paper. ACM SIGKDD Explorations, 2(1), 65-69. ACM, 2000. (postscript) (pdf)

  • A.A. Freitas. On rule interestingness measures. Knowledge-Based Systems journal 12 (5-6), 309-315. Oct. 1999. (pre-print, unformatted version) (postscript) (pdf)

  • S. Lavington, N. Dewhurst, E. Wilkins and A. Freitas. Interfacing knowledge discovery algorithms to large database management systems. Information and Software Technology journal - special issue on Knowledge Discovery and Data Mining, 41(9), 605-617. June 1999. (to get a paper copy, contact me )

              Conference Papers

    2009

  • C.N. Silla Jr. and A.A. Freitas. A global-model naive Bayes approach to the hierarchical prediction of protein functions. In: Proc. Ninth IEEE Int. Conf. on Data Mining (ICDM-2009), pp. 992-997. IEEE Press, 2009. (pdf)

  • C.N. Silla Jr. and A.A. Freitas. Novel top-down approaches for hierarchical classification and their application to automatic music genre classification. In: Proc. 2009 IEEE Int. Conf. on Systems, Man and Cybernetics (SMC-2009), pp. 3499-3504. IEEE Press, 2009. (pdf)

  • F.E.B. Otero, A.A. Freitas and C.G. Johnson. A hierarchical classification ant colony algorithm for predicting gene ontology terms. In Proc. 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBio-2009), Lecture Notes in Computer Science 5483, pp. 68-79. (pdf)

  • F.E.B. Otero and A.A. Freitas and C.G. Johnson. Handling continuous attributes in ant colony classification algorithms. In Proc. 2009 IEEE Symposium on Computational Intelligence in Data Mining (CIDM 2009), pp. 225-231. (pdf)

  • A. Plastino, E.R. Fonseca, R. Fuchshuber, S.L. Martins, A.A. Freitas, M. Luis, S. Salhi. A hybrid data mining metaheuristic for the p-median problem. In Proc. Ninth SIAM Int. Conf. on Data Mining (SDM-2009), pp. 305-316. (pdf)

  • M.P. Basgalupp, R.C. Barros, A.C.P.L.F. de Carvalho, A.A. Freitas and D.D. Ruiz. LEGAL-Tree: a lexicographical multi-objective genetic algorithm for decision tree induction. Proc. 2009 ACM Symposium on Applied Computing (SAC-2009), pp. 1085-1090. (pdf)

    2008

  • F.E.B. Otero, A.A. Freitas and C.G. Johnson. cAnt-Miner: an ant colony classification algorithm to cope with continuous attributes. In: Ant Colony Optimization and Swarm Intelligence (Proc. ANTS-2008), Lecture Notes in Computer Science 5217, pp. 48-59. Springer, 2008. (pdf)

  • E.P. Costa, A.C. Lorena, A.C.P.L.F. de Carvalho, A.A. Freitas. Top-down hierarchical ensembles of classifiers for predicting G-protein-coupled-receptor functions. In: Advances in Bioinformatics and Computational Biology (Proc. BSB-2008), Lecture Notes in Bioinformatics 5167, pp. 35-46. Springer, 2008. (pdf)

  • R.T. Alves, M.R. Delgado, A.A. Freitas. Multi-label hierarchical classification of protein functions with artificial immune systems. In: Advances in Bioinformatics and Computational Biology (Proc. BSB-2008), Lecture Notes in Bioinformatics 5167, pp. 1-12. Springer, 2008. (pdf)

  • A. Secker, M.N. Davies, A.A. Freitas, J. Timmis, E. Clark, D.R. Flower. An artificial immune system for evolving amino acid clusters tailored to protein function prediction. In Proc. 2008 Int. Conf. on Artificial Immune Systems (ICARIS-2008), Lecture Notes in Computer Science 5132, pp. 242-253. Springer, 2008. (pdf)

  • N. Holden and A.A. Freitas. Improving the performance of hierarchical classification with swarm intelligence. In Proc. 6th European Conf. on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBio-2008). Lecture Notes in Computer Science 4973, pp. 48-60. Springer, 2008. (pdf)
    Note: This paper received the Best Paper Award at this conference.

  • M. Iqbal, A.A. Freitas and C.G. Johnson. Protein interaction inference using particle swarm optimization algorithm. In Proc. 6th European Conf. on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBio-2008). Lecture Notes in Computer Science 4973, pp. 61-70. Springer, 2008. (pdf)

    2007

  • E.P. Costa, A.C. Lorena, A.C.P.L.F. Carvalho, A.A. Freitas and N. Holden. Comparing several approaches for hierarchical classification of proteins with decision trees. Advances in Bioinformatics and Computational Biology (Proc. Second Brazilian Symposium on Bioinformatics, BSB-2007), LNBI 4643, pp. 126-137. Springer, 2007. (pdf)

  • E.P. Costa, A.C. Lorena, A.C.P.L.F. Carvalho, and A.A. Freitas. A review of performance evaluation measures for hierarchical classifiers. In: Evaluation Methods for Machine Learning II: papers from the 2007 AAAI Workshop, pp. 1-6. Vancouver, AAAI Press, 2007. (pdf)

  • E.S. Correa, A.A. Freitas and C.G. Johnson. Particle swarm and bayesian networks applied to attribute selection for protein functional classification. In Proc. of the GECCO-2007 Workshop on Particle Swarms: The Second Decade, pp. 2651-2658. ACM Press, 2007. (pdf)

  • N. Holden and A.A. Freitas. A hybrid PSO/ACO algorithm for classification. In Proc. of the GECCO-2007 Workshop on Particle Swarms: The Second Decade, pp. 2745-2750. ACM Press, 2007. (pdf)

  • A. Secker and A.A. Freitas. WAIRS: Improving classification accuracy by weighting attributes in the AIRS classifier. To appear in 2007 Congress on Evolutionary Computation (CEC-2007), Singapore, 2007. (pdf)

    2006

  • G.L. Pappa and A.A. Freitas. Automatically evolving rule induction algorithms. In: Proc. ECML-2006 (17th European Conf. on Machine Learning), LNAI 4212, pp. 341-352. Springer, 2006. (pdf)

  • N. Miles, A.A. Freitas and S. Serjeant. Estimating photometric redshifts using genetic algorithms. In: Applications and Innovations in Intelligent Systems XIV - Proc. of AI-2006, pp. 75-87. Springer, 2006. (pdf)

  • J. Smaldon and A.A. Freitas. Improving the interpretability of classification rules in sparse bioinformatics datasets. In: Research and Development in Intelligent Systems XXIII - Proc. of AI-2006, pp. 377-381. Springer, 2006. (pdf)

  • A. Chan and A.A. Freitas. A new ant colony algorithm for multi-label classification with applications in bioinformatics. In: Proc. Genetic and Evolutionary Computation Conference (GECCO-2006), pp. 27-34. ACM, 2006. (pdf)

  • E.S. Correa, A.A. Freitas and C.G. Johnson. A new discrete particle swarm algorithm applied to attribute selection in a bioinformatics data set. In: Proc. Genetic and Evolutionary Computation Conference (GECCO-2006), pp. 35-42. ACM, 2006. (pdf)

  • J. Smaldon and A.A. Freitas. A new version of the Ant-Miner algorithm discovering unordered rule sets. In: Proc. Genetic and Evolutionary Computation Conference (GECCO-2006), pp. 43-50. ACM, 2006. (pdf)

  • N. Holden and A.A. Freitas. Hierarchical Classification of G-Protein-Coupled Receptors with a PSO/ACO Algorithm. In: Proc. IEEE Swarm Intelligence Symposium (SIS-06), pp. 77-84. IEEE, 2006. (pdf)

    2005

  • A. Chan and A.A. Freitas. A New Classification-Rule Pruning Procedure for an Ant Colony Algorithm. Artificial Evolution (Proc. EA-2005). LNAI 3871, pp. 25-36. Springer, 2005. (pdf)

  • D.R. Carvalho, A.A. Freitas and N. Ebecken. Evaluating the correlation between objective rule interestingness measures and real human interest. Proc. European Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD-2005). LNAI 3721, pp. 453-461. Springer, 2005. (pdf)

  • N. Holden and A.A. Freitas. A hybrid particle swarm/ant colony algorithm for the classification of hierarchical biological data. Proc. 2005 IEEE Swarm Intelligence Symposium, pp. 100-107. IEEE, 2005. (pdf)

  • D.F. Tsunoda, H.S. Lopes and A.A. Freitas. An evolutionary approach for motif discovery and transmembrane protein classification. Applications of Evolutionary Computing (Proc. of EvoBIO-2005: 3rd European Workshop on Evolutionary Bioinformatics), Lecture Notes in Computer Science 3449, pp. 105-114, Springer, 2005. (pdf)

    2004

  • G.L. Pappa and A.A. Freitas. Towards a genetic programming algorithm for automatically evolving rule induction algorithms. Proc. ECML/PKDD-2004 Workshop on Advances in Inductive Rule Learning, 93-108. Pisa, Italy, Sep. 2004. (pdf)

  • C.N. Silla Jr., G.L. Pappa, A.A. Freitas, C.A.A. Kaestner. Automatic text summarization with genetic algorithm-based attribute selection. Advances in Artificial Intelligence (Proc. IX Ibero-American Conf. on Artificial Intelligence - IBERAMIA-2004), LNCS 3315, pp. 305-314, Springer, 2004. (pdf)

  • S.N.M. Ferreira, A.A. Freitas and B.C. Avila. Handling inconsistency in distributed data mining with paraconsistent logic. Proc. 13th Turkish Symp. on Artificial Intelligence and Neural Networks, 19-28. Izmir, Turkey, June 2004. (pdf)

  • R.T. Alves, M.R. Delgado, H.S. Lopes and A.A. Freitas. An artificial immune system for fuzzy-rule induction in data mining. Proc. Parallel Problem Solving from Nature (PPSN-2004), LNCS 3242, pp. 1011-1020, Springer 2004. (pdf)

  • N. Holden and A.A. Freitas. Web page classification with an ant colony algorithm. Proc. Parallel Problem Solving from Nature (PPSN-2004), LNCS 3242, pp. 1092-1102. Springer, 2004. (pdf)

    2003

  • A. Secker, A.A. Freitas and J. Timmis. AISEC: an artificial immune system for e-mail classification. Proc. of the Congress on Evolutionary Computation (CEC-2003), pp. 131-139, Canberra. Australia, December 2003. IEEE Press, 2003. (pdf)

  • D.R. Carvalho, A.A. Freitas, N.F.F. Ebecken. A critical review of rule surprisingness measures. Proc. Data Mining IV - Int. Conf. on Data Mining, pp.545-556, Rio de Janeiro, Brazil, Dec. 2003. WIT Press, 2003. (pdf)

  • C.N. Silla Jr., C.A.A. Kaestner, A.A. Freitas. A non-linear topic detection method for text summarization using Wordnet. Proc. 1st Workshop on Information Technology and Human Language. Sao Carlos - SP, Brazil: ICMC-USP, 2003. (pdf)

  • A.A. Freitas and J. Timmis. Revisiting the foundations of artificial immune systems: a problem-oriented perspective. Artificial Immune Systems: Proc. 2nd Int. Conf. (ICARIS-2003), Lecture Notes in Computer Science 2787, pp. 229-241. Springer-Verlag, 2003. (ps)

  • A. Secker, A.A. Freitas and J. Timmis. A danger theory inspired approach to web mining. Artificial Immune Systems: Proc. 2nd Int. Conf. (ICARIS-2003), Lecture Notes in Computer Science 2787, pp. 156-167. Springer-Verlag, 2003. (pdf)

  • F.E.B. Otero, M.M.S. Silva, A.A. Freitas and J.C. Nievola. Genetic Programming for Attribute Construction in Data Mining. Genetic Programming: Proc. 6th European Conference (EuroGP-2003). Lecture Notes in Computer Science 2610, pp. 384-393. Springer, 2003. (pdf)

  • C.C. Bojarczuk, H.S. Lopes and A.A. Freitas. An innovative application of a constrained-syntax genetic programming system to the problem of predicting survival of patients. Genetic Programming: Proc. 6th European Conference (EuroGP-2003). Lecture Notes in Computer Science 2610, pp. 11-21. Springer, 2003. (pdf)

    2002

  • E. Noda, A.L.V. Coelho, I.L.M. Ricarte, A. Yamakami and A.A. Freitas. Devising adaptive migration policies for cooperative distributed genetic algorithms. Proc. 2002 IEEE Int. Conf. on Systems, Man and Cybernetics (SMC-2002). (Published in CD-ROM.) IEEE Press, 2002. (pdf)

  • G.L. Pappa, A.A. Freitas and C.A.A. Kaestner. A multiobjective genetic algorithm for attribute selection. Proc. 4th Int. Conf. on Recent Advances in Soft Computing (RASC-2002), pp. 116-121. Published in CD-ROM (ISBN: 1-84233-0764). Nottingham Trent University, Nottingham, UK. Dec. 2002. (pdf)

  • O. Larsen, A.A. Freitas and J.C. Nievola. Constructing X-of-N attributes with a genetic algorithm. Proc. 4th Int. Conf. on Recent Advances in Soft Computing (RASC-2002), pp. 326-331. Published in CD-ROM (ISBN: 1-84233-0764). Nottingham Trent University, Notthingham, UK. Dec. 2002. (pdf)

  • D.R. Carvalho and A.A. Freitas. New results for a hybrid decision tree/genetic algorithm for data mining. Proc. 4th Int. Conf. on Recent Advances in Soft Computing (RASC-2002), pp. 260-265. Published in CD-ROM (ISBN: 1-84233-0764), Nottingham Trent University, Notthingham, UK. Dec. 2002. (pdf)

  • G.L. Pappa, A.A. Freitas and C.A.A. Kaestner. Attribute Selection with a Multiobjective Genetic Algorithm. Proc. 16th Brazilian Symposium on Artificial Intelligence (SBIA-2002) - Lecture Notes in Artificial Intelligence 2507, pp. 280-290. Springer-Verlag, 2002. (postscript)

  • J. Larocca Neto, A.A. Freitas and C.A.A. Kaestner. Automatic Text Summarization using a Machine Learning Approach. Proc. 16th Brazilian Symposium on Artificial Intelligence (SBIA-2002) - Lecture Notes in Artificial Intelligence 2507, pp. 205-215. Springer-Verlag, 2002. (pdf)

  • E. Noda, A.A. Freitas and A. Yamakami. A distributed-population genetic algorithm for discovering interesting prediction rules. 7th Online World Conference on Soft Computing (WSC7). Held on the Internet, Sep. 2002. (pdf)

  • W. Romao, A.A. Freitas and R.C.S. Pacheco. A Genetic Algorithm for Discovering Interesting Fuzzy Prediction Rules: applications to science and technology data. Proc. Genetic and Evolutionary Computation Conf. (GECCO-2002), pp. 1188-1195. New York, July 2002. (pdf)

  • D.R. Carvalho and A.A. Freitas. A genetic algorithm with sequential niching for discovering small-disjunct rules. Proc. Genetic and Evolutionary Computation Conf. (GECCO-2002), pp. 1035-1042. New York, July 2002. (pdf)

    2001

  • R.R.F. Mendes, F.B. Voznika, A.A. Freitas and J.C. Nievola. Discovering fuzzy classification rules with genetic programming and co-evolution. Principles of Data Mining and Knowledge Discovery (Proc. 5th European Conf., PKDD 2001) - Lecture Notes in Artificial Intelligence 2168, pp. 314-325. Springer-Verlag, 2001. (postscript)

  • C.E. Bojarczuk, H.S. Lopes and A.A. Freitas. Data mining with constrained-syntax genetic programming: applications in medical data sets. Proc. Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-2001), a Workshop at Medinfo-2001. London, UK, Sep. 2001. (postscript)

  • C.C. Fabris and A.A. Freitas. Incorporating deviation-detection functionality into the OLAP paradigm. Proc. XVI Brazilian Symp. on Databases (SBBD-2001), pp. 274-285. Rio de Janeiro, Brazil. October 2001. (postscript)

  • R.S. Parpinelli, H.S. Lopes and A.A. Freitas. An ant colony based system for data mining: applications to medical data. Proc. 2001 Genetic and Evolutionary Computation Conf. (GECCO-2001), pp. 791-798. Morgan Kaufmann, 2001. (postscript)

  • E.S. Correa, M.T.A. Steiner, A.A. Freitas and C. Carnieri. A genetic algorithm for the P-median problem. Proc. 2001 Genetic and Evolutionary Computation Conf. (GECCO-2001), pp. 1268-1275. Morgan Kaufmann, 2001. (postscript)

  • D.R. Carvalho and A.A. Freitas. An immunological algorithm for discovering small-disjunct rules in data mining. Proc. Graduate Student Workshop at GECCO-2001, pp. 401-404. San Francisco, CA, USA. July 2001. (postscript)

    2000

  • J. Larocca Neto, A.D. Santos, C.A.A. Kaestner, A.A. Freitas. Generating Text Summaries through the Relative Importance of Topics. Proc. Int. Joint Conf.: IBERAMIA-2000 (7th Ibero-American Conf. on Artif. Intel.) & SBIA-2000 (15th Brazilian Symp. on Artif. Intel.) Lecture Notes in Artificial Intelligence 1952, pp. 301-309. Sao Paulo, SP, Brazil. Nov. 2000. (postscript)

  • J. Larocca Neto, A.D. Santos, C.A.A. Kaestner, A.A. Freitas, J.C. Nievola. A trainable algorithm for summarizing news stories. Proc. PKDD-2000 Workshop on Machine Learning and Textual Information Access. Lyon, France. Sep. 2000. (postscript)

  • D.R. Carvalho and A.A. Freitas. A genetic algorithm-based solution for the problem of small disjuncts. Principles of Data Mining and Knowledge Discovery (Proc. 4th European Conf., PKDD-2000. Lyon, France). Lecture Notes in Artificial Intelligence 1910, 345-352. Springer-Verlag, 2000. (postscript)

  • D.R. Carvalho and A.A. Freitas. A hybrid decision tree/genetic algorithm for coping with the problem of small disjuncts in data mining. Proc. 2000 Genetic and Evolutionary Computation Conf. (GECCO-2000), 1061-1068. Las Vegas, NV, USA. July 2000. (postscript)

  • D.L.A. Araujo, H.S. Lopes and A.A. Freitas. Rule discovery with a parallel genetic algorithm. Proc. 2000 Genetic and Evolutionary Computation (GECCO-2000) Workshop Program, 89-92. Las Vegas, NV, USA. July 2000. (postscript)

  • M.V. Fidelis, H.S. Lopes and A.A. Freitas. Discovering comprehensible classification rules with a genetic algorithm. Proc. Congress on Evolutionary Computation - 2000 (CEC-2000), 805-810. La Jolla, CA, USA, July/2000. (postscript)

  • J. Larocca Neto, A.D. Santos, C.A.A. Kaestner, A.A. Freitas. The integrated data mining tool MineKit and a case study of its application on video shop data. Proc. 2nd Int. ICSC Symp. on Engineering of Intelligent Systems (EIS-2000). Scotland, July 2000. ICSC Academic Press. (Published in CD-ROM, ISBN: 3-906454-21-5) (postscript)

  • R. Santos, J.C. Nievola and A.A. Freitas. Extracting comprehensible rules from neural networks via genetic algorithms. Proc. 2000 IEEE Symp. on Combinations of Evolutionary Computation and Neural Networks (ECNN-2000), 130-139. San Antonio, TX, USA. May 2000. (postscript)

  • J. Larocca Neto, A.D. Santos, C.A.A. Kaestner, A.A. Freitas. Document clustering and text summarization. Proc. 4th Int. Conf. Practical Applications of Knowledge Discovery and Data Mining (PADD-2000), 41-55. London: The Practical Application Company. 2000. (postscript)

    1999

  • C.C. Fabris and A.A. Freitas. Discovering surprising patterns by detecting occurrences of Simpson's paradox. In: Research and Development in Intelligent Systems XVI (Proc. ES99, The 19th SGES Int. Conf. on Knowledge-Based Systems and Applied Artificial Intelligence), 148-160. Springer-Verlag, 1999. (postscript)

  • D.L.A. Araujo, H.S. Lopes, A.A. Freitas. A parallel genetic algorithm for rule discovery in large databases. Proc. 1999 IEEE Systems, Man and Cybernetics Conf., v. III, 940-945. Tokyo, Oct. 1999. (postscript)

  • C.S. Fertig, A.A. Freitas, L.V.R. Arruda and C. Kaestner. A Fuzzy Beam-Search Rule Induction Algorithm. Principles of Data Mining and Knowledge Discovery: Proc. 3rd European Conf. (PKDD-99) Lecture Notes in Artificial Intelligence 1704, 341-347. Springer-Verlag, 1999. (postscript)

  • E. Noda, A.A. Freitas, H.S. Lopes. Discovering interesting prediction rules with a genetic algorithm. Proc. Congress on Evolutionary Computation (CEC-99), 1322-1329. Washington D.C., USA, July 1999. (postscript)

  • C.E. Bojarczuk, H.S. Lopes and A.A. Freitas. Discovering comprehensible classification rules using genetic programming: a case study in a medical domain. Proc. Genetic and Evolutionary Computation Conference (GECCO-99) 953-958. Orlando, FL, USA, July 1999. (postscript)

  • A.A. Freitas. A Summary of the Papers Presented at the AAAI-99 & GECCO-99 Workshop on Data Mining with Evolutionary Algorithms: Research Directions. (1-page extended abstract). Proc. of the GECCO-99, Workshop Program, 226. Orlando, FL, USA. July 1999. (postscript)

  • D.R. Carvalho, B.C. Avila, A.A. Freitas. A hybrid genetic algorithm / decision tree approach for coping with unbalanced classes. Proc. 3rd Int. Conf. on the Practical Applications of Knowledge Discovery & Data Mining (PADD-99), 61-70. Londres, April 1999. (postscript)

    1998

  • A.A. Freitas. A genetic algorithm for generalized rule induction. In: R. Roy et al. Advances in Soft Computing - Engineering Design and Manufacturing, 340-353. (Proc. WSC3, 3rd On-Line World Conference on Soft Computing, hosted on the Internet, July 1998.) Springer-Verlag, 1999. (postscript)

  • A.A. Freitas. On objective measures of rule surprisingness. Principles of Data Mining & Knowledge Discovery (Proc. 2nd European Symp., PKDD'98. Nantes, France, Sep. 1998). Lecture Notes in Artificial Intelligence 1510, 1-9. Springer-Verlag, 1998. (postscript)

  • A.A. Freitas. A multi-criteria approach for the evaluation of rule interestingness. Data Mining. (Proc. Int. Conf., Rio de Janeiro, Brazil, Sep. 1998), 7-20. WIT Press, 1998. (postscript)

  • A.A. Freitas. A Survey of Parallel Data Mining. Proc. 2nd Int. Conf. on the Practical Applications of Knowledge Discovery and Data Mining, 287-300. London: The Practical Application Company, Mar. 1998. (postscript)

    1997

  • A.A. Freitas. A genetic programming framework for two data mining tasks: classification and generalized rule induction. Genetic Programming 1997: Proc. 2nd Annual Conf. (Stanford University, July 1997), 96-101. Morgan Kaufmann, 1997. (postscript)

  • A.A. Freitas. Towards large-scale knowledge discovery in databases (KDD) by exploiting parallelism in generic KDD primitives. Proc. 3rd Int. Workshop on Next-Generation Info. Technologies and Systems, 33-43. Neve Ilan, Israel, July 1997. (postscript)

  • A.A. Freitas. The principle of transformation between efficiency and effectiveness: towards a fair evaluation of the cost-effectiveness of KDD techniques. Principles of Data Mining and Knowledge Discovery (Proc. 1st European Symp. Trondheim, Norway. June 1997). Lecture Notes in Artificial Intelligence 1263, 299-306. Springer-Verlag, 1997. (postscript)

    1996

  • A.A. Freitas & S.H. Lavington. A framework for data-parallel knowledge discovery in databases. (Extended Abstract) IEE Colloquium on Knowledge Discovery and Data Mining. Digest No. 96/198, pp.6/1-6/4. London: IEE, Oct./96 (postscript)

  • A.A. Freitas & S.H. Lavington. Speeding up knowledge discovery in large relational databases by means of a new discretization algorithm. In: R. Morrison & J. Kennedy. (Ed.) LNCS 1094: Advances in Databases (Proc. 14th British Nat. Conf. on Databases - BNCOD-14, Edinburgh, UK, July/96), 124-133. Springer-Verlag, 1996. (postscript)

  • A.A. Freitas & S.H. Lavington. Using SQL primitives and parallel DB servers to speed up knowledge discovery in large relational databases. In: R. Trappl. (Ed.) Cybernetics and Systems'96: Proc. 13th European Meeting on Cybernetics and Systems Research, 955-960. Vienna, Apr./96 (postscript)

  • A.A. Freitas & S.H. Lavington. Parallel data mining for very large relational databases. In: H. Liddel et al. (Ed.) LNCS 1067: Proc. Int. Conf. on High-Performance Computing and Networking (HPCN-96, Brussels, Belgium, Apr./96), 158-163. Springer-Verlag, 1996. (postscript)