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Multi-objective Genetic Algorithm Evaluation in Feature Selection

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Book cover Evolutionary Multi-Criterion Optimization (EMO 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6576))

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Abstract

Feature Selection may be viewed as a search for optimal feature subsets considering one or more importance criteria. This search may be performed with Multi-objective Genetic Algorithms. In this work, we present an application of these algorithms for combining different filter approach criteria, which rely on general characteristics of the data, as feature-class correlation, to perform the search for subsets of features. We conducted experiments on public data sets and the results show the potential of this proposal when compared to mono-objective genetic algorithms and two popular filter algorithms.

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Author information

Authors and Affiliations

  1. Grupo Interdisciplinar em Mineração de Dados e Aplicações, Universidade Federal do ABC, Santo André, Brasil

    Newton Spolaôr & Ana Carolina Lorena

  2. Laboratório de Bioinformática, Universidade Estadual do Oeste do Paraná, Foz do Iguaçu, Brasil

    Newton Spolaôr & Huei Diana Lee

Authors
  1. Newton Spolaôr
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  2. Ana Carolina Lorena
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  3. Huei Diana Lee
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Editor information

Editors and Affiliations

  1. Department of Mathematics, Federal University of Minas Gerais, Av. Antônio Carlos 6627, 31270-901, Belo Horizonte, MG, Brazil

    Ricardo H. C. Takahashi

  2. Department of Mechanical Engineering, Indian Institute of Technology Kanpur, 208 016, Kanpur, India

    Kalyanmoy Deb

  3. Department of Computer Engineering, Centro Federal de Educaçao Tecnológica de Minas Gerais, Av. Amazonas 5253, 30421-169, Belo Horizonte, MG, Brazil

    Elizabeth F. Wanner

  4. Faculty of Economics, University of Catania, Corso Italia 55, 95129, Catania, Italy

    Salvatore Greco

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© 2011 Springer-Verlag Berlin Heidelberg

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Spolaôr, N., Lorena, A.C., Lee, H.D. (2011). Multi-objective Genetic Algorithm Evaluation in Feature Selection. In: Takahashi, R.H.C., Deb, K., Wanner, E.F., Greco, S. (eds) Evolutionary Multi-Criterion Optimization. EMO 2011. Lecture Notes in Computer Science, vol 6576. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19893-9_32

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  • DOI: https://doi.org/10.1007/978-3-642-19893-9_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19892-2

  • Online ISBN: 978-3-642-19893-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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