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

Yizhang Jiang, Zhaohong Deng, Fu Lai Korris Chung, Shitong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

52 Citations (Scopus)


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.
Original languageEnglish
Article number7496922
Pages (from-to)145-160
Number of pages16
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number1
Publication statusPublished - 1 Jan 2017


  • Collaborative learning
  • fuzzy classification system (FCS)
  • large margin
  • multiview learning
  • Takagi-Sugeno-Kang (TSK) fuzzy systems

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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