A Method of Generating Rules with a Kernel Fuzzy Classifier
YANG Ai-Min1,2, HU Yun-Fa1
1.Department of Computing and Information Technology, Fudan University, Shanghai 200433 2.Department of Computer Science, Zhuzhou Institute of Technology, Zhuzhou 412008
Abstract A method of generating rules with kernel fuzzy classifier is introduced in this paper. This method selects appropriate kernel function by the principle of SVM. Firstly, the initial sample space is mapped into a high dimensional feature space in order to simplify and separate the samples. Then in the feature space, the dynamic clustering arithmetic dynamically separates the training samples into different clusters and finds out the support vectors of each cluster. For each cluster, a fuzzy rule is defined with ellipsoidal regions. Finally, the rules are tuned by Genetic Algorithms. This method is evaluated by two typical data sets. For the classifier with this method, the learning time is short, and the accuracy and the speed of classification are relatively high.
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2006, Vol. 19 
