Research On Hybrid Approach Of Feature Selection In Bank Credit Scoring

With the financial crisis happened in2007, it is exposed that banks are facing to a mass of credit risks. So credit scoring has attracted more and more attention. Bank has a lot of customer data. Using these data, credit scoring model can judge applicant’s credit risk accurately. But the data is always high-dimensional, and have some irrelevant features. Irrelevant features will decrease classifier’s accuracy. Therefore, feature selection is an important issue.This paper proposes a two-phase hybrid approach based on filter approach and multiple population genetic algorithm-HMPGA. In phase1, it introduces the idea of wrapper approach into three filter approaches to acquire some important prior information for initial population’s setting of MPGA. In phase2, it takes advantage of MPGA’s characteristics of global optimization and quick convergence to find optimal feature subset.This paper uses two real credit scoring data sets of UCI databases to compare HMPGA, MPGA and GA. It verifies that the accuracies of feature subsets acquired from HMPGA, MPGA and GA are superior to three filter approaches. Meanwhile, nonparametric Wilcoxon signed rank test is held to confirm that HMPGA is better than MPGA and GA.HMPGA not only can be applied to feature selection of credit scoring, but also can be applied to more fields of data mining.