The paper presents an evolutionary approach for generating fuzzy rule based classifier. First, a classification problem is divided into several two-class problems following a fuzzy unordered class binarization scheme; next, a fuzzy rule is evolved (not only the condition but the fuzzy sets are evolved (tuned) too) for each two-class problem using a Michigan iterative learning approach; finally, the evolved fuzzy rules are integrated using the fuzzy round robin class binarization scheme. In particular, heaps encoding scheme is used for evolving the fuzzy rules along with a set of special genetic operators (variable length crossover, gene addition and gene deletion). Experiments are conducted with different public available data sets. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Gomez, J. (2004). Evolution of fuzzy rule based classifiers. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3102, 1150–1161. https://doi.org/10.1007/978-3-540-24854-5_112
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