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| Fuzzy Rule-Based Multiple Classifier Fusion |
| Liu Ming; Yuan Bao-zong; Miao Zhen-jiang; Tang Xiao-fang |
| Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China |
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Abstract Nonlinear methods perform well in the multiple classifier fusion. However, the nonlinear methods used for the multiple classifier fusion have poor comprehensibility. As a nonlinear method, the fuzzy rule-based pattern recognition has good comprehensibility, but has not been applied to the multiple classifier fusion. Therefore, this paper introduces fuzzy system to the classifier fusion, where the designing issues for accurate and comprehensible fuzzy system are studied, and an improved support vector based fuzzy rule system designing method is proposed. Experiments have been carried out on four data sets from the ELENA project database and the UCI database. The experimental results show that the proposed method can fuse multiple classifiers with low classification error rate based on comprehensible fuzzy systems.
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Received: 08 December 2005
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2007, Vol. 29 
