Realizing Two-View TSK Fuzzy Classification System by Using Collaborative Learning

In this paper, a novel Takagi-Sugeno–Kang (TSK) fuzzy classification system (FCS) is firstly presented for pattern classification tasks. It is distinguished by having the large margin criterion properly integrated into its objective function. In order to exploit the applicability of fuzzy systems in multiview scenarios, the proposed TSK-FCS is extended to a two-view version, called two-view TSK-FCS (TwoV-TSK-FCS), by using a collaborative learning mechanism. The adopted collaborative learning mechanism not only fully considers the independent information of each view, but also effectively discovers the correlation information hidden in the two views. Thus, the performance of TwoV-TSK-FCS can be enhanced accordingly. Comprehensive experiments on two-view synthetic and UCI datasets demonstrate the effectiveness of the proposed two-view FCS.

I. Introduction

Fuzzy systems have been diversely applied to industrial process control, financial prediction, image processing, medical diagnosis, and so on [1]–[13]. Although the traditional fuzzy systems have shown promising performance in different real-world applications, they are not directly applicable to some new application scenarios where multiview data is involved. In this paper, a two-view Takagi-Sugeno–Kang (TSK) fuzzy system modeling method is proposed to address the classification tasks in multiview data modeling scene.