The evolution of fuzzy classifier for data mining with applications

Authors:
Václav Snášel;Pavel Krömer;Jan Platoš;Ajith Abraham
Affiliations:
Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VŠB, Technical University of Ostrava, Ostrava, Poruba, Czech Republic and Machine Intelligence Research ...;Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VŠB, Technical University of Ostrava, Ostrava, Poruba, Czech Republic;Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VŠB, Technical University of Ostrava, Ostrava, Poruba, Czech Republic;Machine Intelligence Research Labs, Washington
Venue:
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Year:
2010

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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.