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2016 | OriginalPaper | Buchkapitel

Fuzzy Discretization Process from Small Datasets

verfasst von : José M. Cadenas, M. Carmen Garrido, Raquel Martínez

Erschienen in: Computational Intelligence

Verlag: Springer International Publishing

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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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Metadaten
Titel
Fuzzy Discretization Process from Small Datasets
verfasst von
José M. Cadenas
M. Carmen Garrido
Raquel Martínez
Copyright-Jahr
2016
Verlag
Springer International Publishing
DOI
https://doi.org/10.1007/978-3-319-23392-5_15