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Attribute selection with a multiobjective genetic algorithm
G.L. Pappa, A. A. Freitas, and C. A. A. Kaestner
In G. Bittencourt and G.L. Ramalho, editors, Proc. 16th Brazilian Symp. on Artificial Intelligence (SBIA-2002), Lecture Notes in Artificial Intelligence 2507, pages 182-196. Springer-Verlag, November 2002.Abstract
In this paper we address the problem of multiobjective attribute selection in data mining. We propose a multiobjective genetic algorithm (GA) based on the wrapper approach to discover the best subset of attributes for a given classification algorithm, namely C4.5, a well-known decision-tree algorithm. The two objectives to be minimized are the error rate and the size of the tree produced by C4.5. The proposed GA is a multiobjective method in the sense that it discovers a set of non-dominated solutions (attribute subsets), according to the concept of Pareto dominance.
Bibtex Record
@inproceedings{1788, author = {G.L. Pappa and A. A. Freitas and C. A. A. Kaestner}, title = {Attribute Selection with a Multiobjective Genetic Algorithm}, month = {November}, year = {2002}, pages = {182-196}, keywords = {determinacy analysis, Craig interpolants}, note = {}, doi = {}, url = {http://www.cs.kent.ac.uk/pubs/2002/1788}, publication_type = {inproceedings}, submission_id = {23872_1076271147}, ISBN = {3540001247}, booktitle = {Proc. 16th Brazilian Symp. on Artificial Intelligence (SBIA-2002)}, editor = {G. Bittencourt and G.L. Ramalho}, series = {Lecture Notes in Artificial Intelligence 2507}, publisher = {Springer-Verlag}, }