Abstract
Fuzzy classifiers and fuzzy rules can be informally defined as tools that use fuzzy sets or fuzzy logic for their operations. In this paper, we use genetic programming to evolve a fuzzy classifier in the form of a fuzzy search expression to predict product quality. We interpret the data mining task as a fuzzy information retrieval problem and we apply successful information retrieval method for search query optimization to the fuzzy classifier evolution. We demonstrate the ability of genetic programming to evolve useful fuzzy classifiers on a real world case in which a classifier detecting faulty products in an industrial production process is evolved.
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Krömer, P., Snášel, V., Platoš, J., Abraham, A. (2010). Evolving Fuzzy Classifier for Data Mining - an Information Retrieval Approach. In: Herrero, Á., Corchado, E., Redondo, C., Alonso, Á. (eds) Computational Intelligence in Security for Information Systems 2010. Advances in Intelligent and Soft Computing, vol 85. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16626-6_3
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DOI: https://doi.org/10.1007/978-3-642-16626-6_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16625-9
Online ISBN: 978-3-642-16626-6
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