Attribute Selection with a Multiobjective Genetic Algorithm

Pappa, Gisele L. and Freitas, Alex A. and Kaestner, Celso A.A. (2002) Attribute Selection with a Multiobjective Genetic Algorithm. In: Bittencourt, Guilherme and Ramalho, Geber L., eds. Advances in Artificial Intelligence. Lecture Notes in Artificial Intelligence 2507, 1. Springer-Verlag, Berlin pp. 280-290. ISBN 3540001247. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1007/3-540-36127-8_27

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.

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: 17 Jul 2014 08:28
Resource URI: https://kar.kent.ac.uk/id/eprint/13699 (The current URI for this page, for reference purposes)
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