Multi-view TSK Fuzzy System via Collaborative Learning

Conventional fuzzy system modeling methods essentially belong to the single-view learning modality. In multi-view-oriented data scenarios, they can only cope with each view separately, which is prone to incurring their unsatisfactory generalization performance. In response to such problem, the fuzzy system modeling method with the ability of multi-view learning is pursued. To this end, based on the classic L2 norm Takagi-Sugeno-Kang (TSK) fuzzy system, by means of the collaborative learning items qualified for multi-view learning, the core Multi-View TSK Fuzzy System (MV-TSK-FS) modeling method is presented. MV-TSK-FS can not only effectively utilize the independent components composed of the characteristics affiliated to each view, but also take full advantage of the potential information occurred by the interrelated effects among views, which eventually facilitates its relatively strong generalization ability. The experimental results performed on both synthetic and real-life datasets indicate that, compare