Multiobjective optimization of indexes obtained by clustering for feature selection methods evaluation in genes expression microarrays

Authors:
Rodolfo Garcia;Emerson Cabrera Paraiso;Júlio Cesar Nievola
Affiliations:
Post-Graduate Program in Informatics, Pontifical Catholic University of Paraná, Prado Velho, Curitiba, PR-Brazil;Post-Graduate Program in Informatics, Pontifical Catholic University of Paraná, Prado Velho, Curitiba, PR-Brazil;Post-Graduate Program in Informatics, Pontifical Catholic University of Paraná, Prado Velho, Curitiba, PR-Brazil
Venue:
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Year:
2011

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Abstract

The selection of relevant genes in microarray is an important task, since that in a single experiment expressions of thousands of genes are extracted. One way to evaluate feature selection methods in a dataset is by clustering the instances that have similar behaviors. The aim of this paper is to use a set of indexes that measure the quality of a clustering and, through the multiobjective optimization of this set, to show how it is possible to find the best feature selection methods in genes expression datasets obtained by microarray technique.