An Ensemble of Fuzzy Sets and Least Squares Support Vector Machines Approach to Consumer Credit Risk Assessment

Least squares support vector machines (LS-SVM), with excellent generalization performance and low computational cost, has been proven to be a useful tool in consumer credit risk assessment. It is a common assumption that the labels of the consumers are unchanged, which is contradictory with population drift. In this paper, we use a fuzzy membership of each input data to represent the impact of population drift on consumers´ labels and the relative importance for the construction of the separating decision function, which is an ensemble of fuzzy sets and sparse LS-SVM. The purpose is to try to explain why an applicant should be rejected. Two UCI and an American credit card datasets are used to test the efficiency of our method and the result proves to be a satisfactory one.