Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/65835
Title: Realizing two-view TSK fuzzy classification system by using collaborative learning
Authors: Jiang, Y
Deng, Z
Chung, FL
Wang, S
Keywords: Collaborative learning
Fuzzy classification system (FCS)
Large margin
Multiview learning
Takagi-Sugeno-Kang (TSK) fuzzy systems
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on systems, man, and cybernetics. Part A, Systems and humans, 2017, v. 47, no. 1, 7496922, p. 145-160 How to cite?
Journal: IEEE transactions on systems, man, and cybernetics. Part A, Systems and humans 
Abstract: 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.
URI: http://hdl.handle.net/10397/65835
ISSN: 1083-4427
EISSN: 1083-4419
DOI: 10.1109/TSMC.2016.2577558
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