A New Classifier Based On AFS Fuzzy Logic

AFS theory is a new method to study fuzzy set which was proposed by professor Xiaodong Liu in 1995. AFS theory have studied and discussed the essential problems of fuzzy theory: How to find the strict and consistent algorithms of determining membership functions for fuzzy concepts and the fuzzy logical operations accurately representing human thinking logic. In the framework of AFS (Axiomatic Fuzzy Set) theory, AFS present a new algorithm of determining membership functions for fuzzy concepts according to original data and information and propose AFS fuzzy logic. In fact, AFS fuzzy logic is more appropriate to represent human thinking logic and the models of intelligent systems in real world applications based on original data and information, which are comprehensible and have definitive semantic meanings. These approaches have potential applications in the study of recognition and large-scale complicate intelligent systems. Recently, AFS theory has been developed further and applied to fuzzy clustering analysis, fuzzy decision trees, credit rating analysis , pattern recognition and hitch diagnoses, etc.This paper propose a new classifier based on AFS fuzzy logic and fuzzy entropy. This recognition approach can mimic the cognitive process of human reasoning for pattern classification. This classifier uses the original data directly, and gets the description of each class. These descriptions are interpretable and understandable. Then it applies the new classifier to the real data set, and gets very good results. Using MATLAB, we can get the corresponding figure and results. Finally, we compare the results abtained by the classifier proposed with the results abtained by the other algorithm. The results present the proposed classifier is practical and useful.