GA-KDE-Bayes: An evolutionary wrapper method based on non-parametric density estimation applied to bioinformatics problems

Maria Fernanda Wanderley, Vincent Gardeux, René Natowicz, Antônio P. Braga

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

Abstract

This paper presents an evolutionary wrapper method for feature selection that uses a non-parametric density estimation method and a Bayesian Classifier. Non-parametric methods are a good alternative for scarce and sparse data, as in Bioinformatics problems, since they do not make any assumptions about its structure and all the information come from data itself. Results show that local modeling provides small and relevant subsets of features when comparing to results available on literature.

Original languageEnglish (US)
Title of host publicationESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Pages155-160
Number of pages6
StatePublished - Nov 11 2013
Event21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013 - Bruges, Belgium
Duration: Apr 24 2013Apr 26 2013

Publication series

NameESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Other

Other21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013
CountryBelgium
CityBruges
Period4/24/134/26/13

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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  • Cite this

    Wanderley, M. F., Gardeux, V., Natowicz, R., & Braga, A. P. (2013). GA-KDE-Bayes: An evolutionary wrapper method based on non-parametric density estimation applied to bioinformatics problems. In ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 155-160). (ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).