Application of Gray Wolf Optimization Algorithm on Synchronous Processing of Sample Equalization and Feature Selection in Credit Evaluation

Abstract

With the rapid development of Internet finance industry, traditional credit risk evaluation is facing challenges in the face of massive data.Due to the unbalanced sample categories and high feature redundancy in credit evaluation, it has become the key factor affecting the classification accuracy of current evaluation.In order to solve the above problems, a method based on gray wolf optimization algorithm is proposed to process the samples under sampling and feature selection synchronously.In this me-thod, the performance of the classifier is taken as the heuristic information of the gray wolf optimization algorithm, and then the intelligent search is carried out to obtain the combination of the optimal sample and the feature set, and the tabu table strategy is introduced into the original gray wolf algorithm to avoid the algorithm falling into the local optimum.Experimental results show that the proposed method has a great improvement compared with other methods, and its performance on different data sets proves that it can effectively solve the problem of sample imbalance, reduce the dimension of feature space, and improve the accuracy of classification.Compared with the original data, the accuracy of credit risk evaluation is improved by about 3%, which proves the applicability and superiority of this method in the field of credit evaluation.

Keywords

• credit evaluation|sample imbalance|feature selection|gray wolf optimization algorithm