School of Computing

Attribute selection with a multiobjective genetic algorithm

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

In G. Bittencourt and G.L. Ramalho, editors, Proc. 16th Brazilian Symp. on Artificial Intelligence (SBIA-2002), Lecture Notes in Artificial Intelligence 2507, pages 182-196. Springer-Verlag, November 2002.

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.



Bibtex Record

@inproceedings{1788,
author = {G.L. Pappa and A. A. Freitas and C. A. A. Kaestner},
title = {Attribute Selection with a Multiobjective Genetic Algorithm},
month = {November},
year = {2002},
pages = {182-196},
keywords = {determinacy analysis, Craig interpolants},
note = {},
doi = {},
url = {http://www.cs.kent.ac.uk/pubs/2002/1788},
    publication_type = {inproceedings},
    submission_id = {23872_1076271147},
    ISBN = {3540001247},
    booktitle = {Proc. 16th Brazilian Symp. on Artificial Intelligence (SBIA-2002)},
    editor = {G. Bittencourt and G.L. Ramalho},
    series = {Lecture Notes in Artificial Intelligence 2507},
    publisher = {Springer-Verlag},
}

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