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European Conference on the Applications of Evolutionary Computation

EvoApplications 2017: Applications of Evolutionary Computation pp 506–521Cite as

Feature Selection in High Dimensional Data by a Filter-Based Genetic Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10199))

Abstract

In classification and clustering problems, feature selection techniques can be used to reduce the dimensionality of the data and increase the performances. However, feature selection is a challenging task, especially when hundred or thousands of features are involved. In this framework, we present a new approach for improving the performance of a filter-based genetic algorithm. The proposed approach consists of two steps: first, the available features are ranked according to a univariate evaluation function; then the search space represented by the first M features in the ranking is searched using a filter-based genetic algorithm for finding feature subsets with a high discriminative power.

Experimental results demonstrated the effectiveness of our approach in dealing with high dimensional data, both in terms of recognition rate and feature number reduction.

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Notes

  1. 1.

    Note that the same holds also for the feature-class correlation.

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Authors and Affiliations

  1. Dipartimento di Ingegneria Elettrica e dell’Informazione (DIEI), Università di Cassino e del Lazio meridionale, Via G. Di Biasio, 43 02043, Cassino, Frosinone, Italy

    Claudio De Stefano, Francesco Fontanella & Alessandra Scotto di Freca

Authors
  1. Claudio De Stefano
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  2. Francesco Fontanella
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  3. Alessandra Scotto di Freca
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Corresponding author

Correspondence to Francesco Fontanella .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

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De Stefano, C., Fontanella, F., Scotto di Freca, A. (2017). Feature Selection in High Dimensional Data by a Filter-Based Genetic Algorithm. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10199. Springer, Cham. https://doi.org/10.1007/978-3-319-55849-3_33

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  • DOI: https://doi.org/10.1007/978-3-319-55849-3_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55848-6

  • Online ISBN: 978-3-319-55849-3

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