Fuzzy weighted support vector regression with a fuzzy partition

The problem of the traditional support vector regression (SVR) approach, referred to as the global SVR approach, is the incapability of interpreting local behavior of the estimated models. An approach called the local SVR approach was proposed in the literature to cope with this problem. Although the local SVR approach can indeed model local behavior of models better than the global SVR approach does, the local SVR approach still has the problem of boundary effects, which may generate a large bias at the boundary and also need more time to calculate. In this paper, the fuzzy weighted SVR with a fuzzy partition is proposed. Because the concept of locally weighted regression is not used in the proposed approach, the boundary effects will not appear. The proposed method first employs the fuzzy c-mean clustering algorithm to split training data into several training subsets. Then, the local-regression models (LRMs) are independently obtained by the SVR approach for each training subset. Finally, those LRMs are combined by a fuzzy weighted mechanism to form the output. Experimental results show that the proposed approach needs less computational time than the local SVR approach and can have more accurate results than the local/global SVR approaches does.