中文摘要：

在一定的约束条件下,提出并证明误分类代价敏感SVM（MC-SVM）与一类基于规则的FIS的函数具有等效性.在此基础上,提出了基于MC-SVM学习过程的FIS（MC-MBFIS）的设计方法.MC-MBFIS继承了基于规则的FIS的显式推理能力,也继承了MC-SVM的代价敏感性.Benchmark数据实验表明,MC-MBFIS能降低平均误分类代价.

英文摘要：

Under some restrictions,the functional equivalence between misclassification cost-sensitive support vector machines（MC-SVM） and rule-based fuzzy inference system（FIS） is proposed. Then based on the learning mechanism of MC-SVM,the algorithm of designing a rule-based FIS,misclassification cost-sensitive mercer binary FIS （MC-MBFIS）,is given. The MC-MBFIS algorithm has the good generalization ability,can avoid the ＂curse of dimension＂ ,and has the transparent inference ability. Experimental results based on a few benchmark data sets show that the MC-MBFIS algorithm can reduce the average misclassification cost.