Combination of feature selection and optimized fuzzy apriori rules: the case of credit scoring

Credit scoring is an important topic and banks collect different data from their loan applicants to make appropriate and correct decisions. Rule bases are favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants. This paper, uses four feature selection approaches as features pre-processing combined with fuzzy apriori. These methods are stepwise regression, Classification And Regression Tree (CART), correlation matrix and Principle Component Analysis (PCA). Particle Swarm is applied to find the best fuzzy apriori rules by searching different support and confidence. Considering Australian and German University of California at Irvine (UCI) and an Iranian bank datasets, different feature selections methods are compared in terms of accuracy, number of rules and number of features. The results are compared using T test; it reveals that fuzzy apriori combined with PCA creates a compact rule base and shows better results than the single fuzzy apriori model and other combined feature selection methods.