Pappa, G.L and Freitas, A.A. and Kaestner, Celso A.A. (2002) Attribute Selection with a Multiobjective Genetic Algorithm. In: Bittencourt, G. and Ramalho, G.L., eds. Advances in Artificial Intelligence. Lecture Notes in Artificial Intelligence 2507, 1. Springer-Verlag, Berlin pp. 280-290. ISBN 3540001247.
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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.
|Item Type:||Conference or workshop item (Paper)|
|Uncontrolled keywords:||data mining, attribute selection, multiobjective genetic algorithms|
|Subjects:||Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group|
|Depositing User:||Mark Wheadon|
|Date Deposited:||24 Nov 2008 17:59|
|Last Modified:||18 Jul 2012 08:52|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/13699 (The current URI for this page, for reference purposes)|
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