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Attribute Selection with a Multiobjective Genetic Algorithm

Pappa, Gisele L., Freitas, Alex A., Kaestner, Celso A.A. (2002) Attribute Selection with a Multiobjective Genetic Algorithm. In: Bittencourt, Guilherme and Ramalho, Geber L., eds. Proc. 16th Brazilian Symp. on Artificial Intelligence (SBIA-2002). Lecture Notes in Artificial Intelligence 2507 , 1. pp. 280-290. Springer-Verlag, Berlin ISBN 3-540-00124-7. (doi:10.1007/3-540-36127-8_27) (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:13699)

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.
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)
DOI/Identification number: 10.1007/3-540-36127-8_27
Uncontrolled keywords: data mining, attribute selection, multiobjective genetic algorithms
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 Nov 2021 09:51 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/13699 (The current URI for this page, for reference purposes)

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