A multiobjective genetic algorithm for attribute selection

Pappa, G.L. and Freitas, A.A. and Kaestner, Celso A.A. (2002) A multiobjective genetic algorithm for attribute selection. In: Proc. 4th Int. Conf. on Recent Advances in Soft Computing (RASC-2002), 12 & 13 December 2002 , Nottingham, United Kingdom. (The full text of this publication is not available from this repository)

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Abstract

The problem of feature selection in data mining is an important real-world problem that involves multiple objectives to be simultaneously optimized. In order to tackle this problem this work proposes a multiobjective genetic algorithm for feature selection based on the wrapper approach. The algorithm’s main goal is to find the best subset of features that minimizes both the error rate and the size of the tree discovered by a classification algorithm, namely C4.5, using the Pareto dominance concept

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: attribute selection, data mining, multiobjective genetic algorithm
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/13687 (The current URI for this page, for reference purposes)
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