Fuzzy $(c+p)$-Means Clustering and Its Application to a Fuzzy Rule-Based Classifier: Toward Good Generalization and Good Interpretability
Jacek M. Łęski · IEEE Transactions on Fuzzy Systems · 2014
This paper introduces a new classifier design method based on a modification of the classical fuzzy c-means clustering. First, a new fuzzy c-means clustering with p constant prototypes is proposed. This method can be considered a generalization of the concept of the conditional fuzzy clustering with some prototypes a priori known. A special initialization of the prototypes is introduced. Next, the proposed clustering method is used to construct the premises of an IF-THEN 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 IF-THEN 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.