In order to improve the interpretability and effectiveness of the fuzzy classifier rules, this paper presents a new method to extract the fuzzy rules based on the maximum ball only containing the homogeneous data. At first, every sample constructs a maximum ball in the light of the shortest distance to heterogeneous samples. Then those balls are reduced according to the relation of inclusion and the unique among the samples that the ball encloses. Then the fuzzy rules are constructed with the reserved balls. The parameters learning of the antecedent part of the classifier are based on the minimization of the weight misclassification quadratic error and resolved with the conjugate gradient algorithm. The experiments on 12 benchmark datasets with 10 folds are performed to demonstrate the validity of the classifier.
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XU Mingliang, WANG Shitong. Extracting Fuzzy Rules from the Maximum Ball Containing the Homogeneous Data. JEIT, 2017, 39(5): 1130-1135.
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