Realizing Two-View TSK Fuzzy Classification System by Using Collaborative Learning
提出一种集成大间隔准则的TSK模糊分类系统，并扩展为双视图版本，通过协作学习利用视图独立与关联信息提升分类性能，实验验证了有效性。
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.