Fuzzy src='/images/tex/30150.gif' alt='(c+p)'> -Means Clustering and Its Application to a Fuzzy Rule-Based Classifier: Toward Good Generalization and Good Interpretability

摘要

This paper introduces a new classifier design method based on a modification of the classical fuzzy -means clustering. First, a new fuzzy -means clustering with constant prototypes is proposed. This method can be considered a generalization of the concept of the conditional fuzzy clustering with some prototypes known. A special initialization of the prototypes is introduced. Next, the proposed clustering method is used to construct the premises of an rule-based classifier. The conclusions of these rules are obtained by minimization of a criterion function with various approximations of a misclassification error (e.g., based on the quadratic, the linear, the sigmoidal or the Huber's loss function). The conjugate gradient algorithm is used to minimize the proposed criterion function. Each rule is represented in the Mamdani–Assilan form, which has good interpretability. Finally, an extensive experimental analysis on 14 benchmark datasets is performed to demonstrate the validity of the classifier introduced. Its competitiveness to the state-of-the-art classifiers, with respect to both performance and interpretability, is also shown.