The Application Of AFS Fuzzy Logic In The Design Of Classifier

Along with the mature application of data-base technique and the rapid development of the Internet technology, the quantity of data in people's daily lives is increasing rapidly byexponential. Therefore we need a new method to find the deeper rule which can help provide more efficient decision support since people are not satisfied with conventional searching and statistics analysis method anymore. Then, more and more classification problems appear in our daily lives, such as medicine, agriculture and security field etc. In order to solve the practical problems better, many methods have been tried for designing the classifier, such as the method like fuzzy logic, genetic algorithm, decision tree algorithms and neural network algorithm etc, which have achieved success to some degree, are mainly applied in classifier design. Since the application field is becoming wider and wider, the study on classification problem has brighter future and make the study more meaningful.According to the shorcomings of existing classifier based on AFS fuzzy logic, a new classifier based on AFS (Axiomatic Fuzzy Set) fuzzy logic was designed. The classifier has several advantages, such as short and accurate description, small time consumption,easier sentences for better understanding, relatively high accuracy. Experimental results demonstrated that a high accuracy can be achieved using the proposed classification algorithm by applying only the order relations of the attributes, and the numerical representations of the attribute are not necessary compositions. Thus, this algorithm is widely applicable, and that it can retain more information of the original data.Experiments of classification were done on three famous datasets: wine, iris and WBCD (Wisconsin breast cancer data), which were obtained from database of UCI (University of California, Irvine). After analying experiment results, we found out some problems existed in the AFS Logic algorithm, such as the easy missing problem in case of multi-parameters combinations and that some of the descriptions were difficult to understand. For these problems, modification and improvement of algorithm were done and better performances were achieved from the new experiments on four datasets. At last, through the compare with other algorithms, the advantages of this classification algorithm were shown.