A new design method for linguistically understandable fuzzy classifier

Many classification methods have been reported and the most popular ones among them are multilayer perceptron (MLP), nearest neighbor (NN), and support vector machine (SVM), etc. All of them have the weakness that they are not transparent or not clearly understandable to human beings. Sometimes, however, linguistically understandable classifiers could be preferred to the nontransparent models. Especially, when we are given a large set of data and we have to draw concise but interpretable hypothesis or conclusion, linguistically understandable classifiers should be required. In this paper, a linguistically understandable fuzzy classifier is presented and a new training method is proposed. To handle the uncertainties stemming from the problem or the measurement, the fuzzy classifier, the consequent part outputs the degree of truth for the assignment of each fuzzy set to the classes.