Supervised Clustering and Fuzzy Decision Tree Induction for the Identification of Compact Classifiers
نویسندگان
چکیده
Fuzzy decision tree induction algorithms require the fuzzy quantization of the input variables. This paper demonstrates that supervised fuzzy clustering combined with similarity-based rule-simplification algorithms is an effective tool to obtain the fuzzy quantization of the input variables, so the synergistic combination of supervised fuzzy clustering and fuzzy decision tree induction can be effectively used to build compact and accurate fuzzy classifiers. Fuzzy Decision Trees, Fuzzy Clustering, Input Quantization, Fuzzy Classifier
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