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 th...

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Bibliographic Details
Published in:IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 37; no. 3; pp. 630 - 640
Main Author: Chuang, Chen-Chia
Format: Journal Article
Language:English
Published: United States IEEE 01.06.2007
Subjects:
Algorithms
Associate members
Clustering algorithms
Computer Simulation
Fuzzy c-mean (FCM) clustering algorithm
Fuzzy Logic
fuzzy weighted mechanism
Models, Statistical
Optimal control
Partitioning algorithms
Pattern Recognition, Automated - methods
Quadratic programming
Regression Analysis
Support vector machine classification
Support vector machines
support vector regression (SVR)
Training data
Upper bound
ISSN:1083-4419
Online Access:Get full text
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Summary: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
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ISSN:1083-4419
DOI:10.1109/TSMCB.2006.889611