A novel fuzzy SVM based on fuzzy c-means for credit scoring

Credit scoring has become a very important issue in the financial industry. In order to identify good or bad credit applicants, many literatures with their proposed classification methods had been published to deal with this problem. The support vector machine (SVM) of statistical learning theory was successfully applied in classification. However it still suffers from noise sensitivity originating from the fact that all the data points are treated equally. To tackle this problem, the SVM was extended into a fuzzy SVM (FSVM) by the introduction of fuzzy memberships. In this paper, a new membership calculation method is proposed, which uses a fuzzy c-means (FCM) algorithm to deal with the classification problems with outliers or noises. In the FCM-FSVM algorithm, the training dataset is divided into many clusters by using FCM, then the membership of each of training points belongs to its class can be used to be the membership of FSVM. The proposed algorithm was applied to a credit scoring classification problem, and the results verified the effectiveness of the method.