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

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Applications of Evolutionary Computation (EvoApplications 2017)

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

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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

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  • Online ISBN: 978-3-319-55849-3

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