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A multiobjective genetic algorithm for attribute selection

Pappa, Gisele L. and Freitas, Alex A. and Kaestner, Celso A.A. (2002) A multiobjective genetic algorithm for attribute selection. In: Lofti, Ahmed and Garibaldi, Jon and John, Robert, eds. Proceedings Of The 4th International Conference On Recent Advances In Soft Computing. Nottingham Trent University, pp. 116-121. ISBN 1-84233-076-4. (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) (KAR id:13687)

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)

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: Book section
Uncontrolled keywords: attribute selection, data mining, multiobjective genetic algorithm
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 17:59 UTC
Last Modified: 16 Feb 2021 12:24 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/13687 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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