The conventional classification algorithm generally pursuing more highly accuracy, fuzzy Inference System (FIS) and Support Vector Machines (SVM) included, do not perform well when the misclassification cost of one class samples is unequal to that of another. Under some restrictions, firstly, the functional equivalence between Misclassification Cost-sensitive SVM (MC-SVM) and rule-based FIS is proposed. Secondly, based on the learning mechanism of MC-SVM, the algorithm of designing a rule-based FIS, called 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 reduced average misclassification cost.