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Fuzzy Discretization Process from Small Datasets

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Computational Intelligence (IJCCI 2013)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 613))

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Abstract

A classification problem involves selecting a training dataset with class labels, developing an accurate description or a model for each class using the attributes available in the data, and then evaluating the prediction quality of the induced model. In this paper, we focus on supervised classification and models which have been obtained from datasets with few examples in relation with the number of attributes. Specifically, we propose a fuzzy discretization method of numerical attributes from datasets with few examples. The discretization of numerical attributes can be a crucial step since there are classifiers that cannot deal with numerical attributes, and there are other classifiers that exhibit better performance when these attributes are discretized. Also we show the benefits of the fuzzy discretization method from dataset with few examples by means of several experiments. The experiments have been validated by means of statistical tests.

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Acknowledgments

Supported by the project TIN2011-27696-C02-02 of the Ministry of Economy and Competitiveness of Spain. Thanks also to “Fundación Séneca - Agencia de Ciencia y Tecnología de la Región de Murcia” (Spain) for the support given to Raquel Martínez by the scholarship program FPI.

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Authors and Affiliations

  1. Faculty of Informatics, University of Murcia, Campus of Espinardo, 30100, Murcia, Spain

    José M. Cadenas, M. Carmen Garrido & Raquel Martínez

Authors
  1. José M. Cadenas
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  2. M. Carmen Garrido
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  3. Raquel Martínez
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Corresponding author

Correspondence to Raquel Martínez .

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Editors and Affiliations

  1. Images, Signals and Intelligence Systems Laboratory, University PARIS-EST Creteil (UPEC), Créteil, France

    Kurosh Madani

  2. Departamento de Engenharia Informatica Polo II - Pinhal de Marrocos, University of Coimbra, Coimbra, Portugal

    António Dourado

  3. aSEEB-ISR-IST Av. Rovisco Pais, 1, Technical University of Lisbon (IST), Lisbon, Portugal

    Agostinho Rosa

  4. Rua do Vale de Chaves, Estefanilha, Polytechnic Institute of Setúbal / INSTICC, Setúbal, Portugal

    Joaquim Filipe

  5. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

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Cadenas, J.M., Garrido, M.C., Martínez, R. (2016). Fuzzy Discretization Process from Small Datasets. In: Madani, K., Dourado, A., Rosa, A., Filipe, J., Kacprzyk, J. (eds) Computational Intelligence. IJCCI 2013. Studies in Computational Intelligence, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-23392-5_15

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  • DOI: https://doi.org/10.1007/978-3-319-23392-5_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23391-8

  • Online ISBN: 978-3-319-23392-5

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