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A Noise Clustering-induced Robust Adaptive Network-based Fuzzy Inference System for Classification

Adaptive Network-based Fuzzy Inference System (ANFIS) is a neural network-based model for fuzzy inference system (FIS) and can be a promising approach for explainable neural networks. In this paper, robustification of ANFIS is considered from the classification application viewpoint, where the noise rejection mechanism is introduced induced by noise fuzzy clustering. In real world classification tasks, we often suffer from unreliable class labels, where some objects have incorrect class labels and should be rejected from classifier construction. Noise fuzzy clustering proposed by Davé is an extension of fuzzy c-means (FCM) to robust clustering, where an additional noise cluster works for absorbing noise objects. Because the noise clustering scheme with single cluster cases can be identified with robust least square estimation, the robustifying mechanism has been applied to several least square-type data analyses. In this paper, the noise clustering scheme is introduced into the ANFIS classification model, where non-noise fuzzy memberships are utilized such that the additional noise cluster absorbs noise objects and the ANFIS classifier is robustly constructed by rejecting noise objects. The minimization of the membership-weighted least square objective function and the estimation of non-noise fuzzy memberships are iteratively implemented until convergence. The characteristics of the proposed method are demonstrated through numerical experiments using real world benchmark datasets.