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A Novel Feature Selection Method for Classification Using a Fuzzy Criterion

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7997))

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Abstract

Although many classification methods take advantage of fuzzy sets theory, the same cannot be said for feature reduction methods. In this paper we explore ideas related to the use of fuzzy sets and we propose a novel fuzzy feature selection method tailored for the Regularized Generalized Eigenvalue Classifier (ReGEC). The method provides small and robust subsets of features that can be used for supervised classification. We show, using real world datasets that the performance of ReGEC classifier on the selected features well compares with that obtained using them all.

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Acknowledgment

This work has been partially funded by Italian Flagship project Interomics and Kauno Technologijos Universitetas (KTU).

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Authors and Affiliations

  1. High Performance Computing and Networking Institute, National Research Council, Naples, Italy

    Maria Brigida Ferraro & Mario Rosario Guarracino

  2. Department of European and Mediterranean Studies, Second University of Naples, Caserta, Italy

    Antonio Irpino & Rosanna Verde

  3. Department of Informatics, Kaunas University of Technology, Kaunas, Lithuania

    Mario Rosario Guarracino

Authors
  1. Maria Brigida Ferraro
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  2. Antonio Irpino
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  3. Rosanna Verde
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  4. Mario Rosario Guarracino
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Corresponding author

Correspondence to Mario Rosario Guarracino .

Editor information

Editors and Affiliations

  1. Università Catania Dipto. Matematica e Informatica, Catania, Italy

    Giuseppe Nicosia

  2. Industrial & Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos Pardalos

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Ferraro, M.B., Irpino, A., Verde, R., Guarracino, M.R. (2013). A Novel Feature Selection Method for Classification Using a Fuzzy Criterion. In: Nicosia, G., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2013. Lecture Notes in Computer Science(), vol 7997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44973-4_49

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  • DOI: https://doi.org/10.1007/978-3-642-44973-4_49

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-44972-7

  • Online ISBN: 978-3-642-44973-4

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