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 a successful information retrieval method for search query optimization to the fuzzy classifier evolution. We demonstrate the ability of the genetic programming to evolve useful fuzzy classifiers on two use cases in which we detect faulty products of a product processing plant and discover intrusions in a computer network.
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Snášel, V., Krömer, P., Platoš, J., Abraham, A. (2010). The Evolution of Fuzzy Classifier for Data Mining with Applications. In: Deb, K., et al. Simulated Evolution and Learning. SEAL 2010. Lecture Notes in Computer Science, vol 6457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17298-4_36
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DOI: https://doi.org/10.1007/978-3-642-17298-4_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17297-7
Online ISBN: 978-3-642-17298-4
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