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OFP_CLASS: a hybrid method to generate optimized fuzzy partitions for classification

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Abstract

The discretization of values plays a critical role in data mining and knowledge discovery. The representation of information through intervals is more concise and easier to understand at certain levels of knowledge than the representation by mean continuous values. In this paper, we propose a method for discretizing continuous attributes by means of fuzzy sets, which constitute a fuzzy partition of the domains of these attributes. This method carries out a fuzzy discretization of continuous attributes in two stages. A fuzzy decision tree is used in the first stage to propose an initial set of crisp intervals, while a genetic algorithm is used in the second stage to define the membership functions and the cardinality of the partitions. After defining the fuzzy partitions, we evaluate and compare them with previously existing ones in the literature.

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Acknowledgments

Supported by the projects TIN2008-06872-C04-03 and TIN2011-27696-C02-02 of the MICINN of Spain and European Fund for Regional Development. Thanks also to “Fundación Séneca” (Spain) for the Funding Program for Research Groups of Excellence (04552/GERM/06) and the support given to R. Martínez by FPI scholarship program.

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Authors and Affiliations

  1. Dept. de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain

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

  2. GE Global Research, One Research Circle, Niskayuna, NY, 12309, USA

    Piero P. Bonissone

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  1. Jose M. Cadenas
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  2. M. Carmen Garrido
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  3. Raquel Martínez
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  4. Piero P. Bonissone
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Correspondence to Jose M. Cadenas.

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Cadenas, J.M., Garrido, M.C., Martínez, R. et al. OFP_CLASS: a hybrid method to generate optimized fuzzy partitions for classification. Soft Comput 16, 667–682 (2012). https://doi.org/10.1007/s00500-011-0778-0

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