School of Computing

A multiobjective genetic algorithm for attribute selection

G. L. Pappa, A. A. Freitas, and C. A. A. Kaestner

In A. Lofti, J. Garibaldi, and R. John, editors, Proc. 4th Int. Conf. on Recent Advances in Soft Computing (RASC-2002), pages 182-196. Nottingham Trent University, December 2002.

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.



Bibtex Record

@inproceedings{1789,
author = {G. L. Pappa and A. A. Freitas and C. A. A. Kaestner},
title = {A Multiobjective Genetic Algorithm for Attribute Selection},
month = {December},
year = {2002},
pages = {182-196},
keywords = {determinacy analysis, Craig interpolants},
note = {},
doi = {},
url = {http://www.cs.kent.ac.uk/pubs/2002/1789},
    publication_type = {inproceedings},
    submission_id = {24019_1076271586},
    ISBN = {1842330764},
    booktitle = {Proc. 4th Int. Conf. on Recent Advances in Soft Computing (RASC-2002)},
    editor = {A. Lofti and J. Garibaldi and R. John},
    publisher = {Nottingham Trent University},
}

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