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Exploration of Two-Objective Scenarios on Supervised Evolutionary Feature Selection: A Survey and a Case Study (Application to Music Categorisation)

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Evolutionary Multi-Criterion Optimization (EMO 2015)

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Abstract

Almost all studies which apply feature selection for supervised classification are limited to single-objective optimisation, validating feature sets with only one criterion like accuracy, classification error, correlation with the category, etc. However, this approach usually leads to a decrease of performance with respect to other relevant criteria. In this paper, we provide a summary of previous studies on supervised evolutionary multi-objective feature selection with a focus on the choice of the objectives. Further, we explore the application of EMO-FS for 28 pairs of evaluation measures in a case study predicting musical genres and styles based on the initial set of 636 features. To measure the advantage of a multi-objective approach over a single-objective one, we propose two metrics based on hypervolume and provide a statistical comparison of multi-objective performance across 14 categorisation tasks.

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Author information

Authors and Affiliations

  1. Department of Computer Science, TU Dortmund, Dortmund, Germany

    Igor Vatolkin

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  1. Igor Vatolkin
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Correspondence to Igor Vatolkin .

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Editors and Affiliations

  1. Institute for Polymers and Composites/I3N University of Minho Guimarães Portugal, Guimaraes, Portugal

    António Gaspar-Cunha

  2. Dept. of Electrical and Computer Engg, University of Coimbra, Coimbra, Portugal

    Carlos Henggeler Antunes

  3. CINVESTAV-IPN Depto. de Computacíon, Col. San Pedro Zacatenco, México DF, Mexico

    Carlos Coello Coello

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Vatolkin, I. (2015). Exploration of Two-Objective Scenarios on Supervised Evolutionary Feature Selection: A Survey and a Case Study (Application to Music Categorisation). In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C. (eds) Evolutionary Multi-Criterion Optimization. EMO 2015. Lecture Notes in Computer Science(), vol 9019. Springer, Cham. https://doi.org/10.1007/978-3-319-15892-1_36

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  • DOI: https://doi.org/10.1007/978-3-319-15892-1_36

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