A hybrid model with novel feature selection method and enhanced voting method for credit scoring

Author:

Yao Jianrong1,Wang Zhongyi1,Wang Lu1,Zhang Zhebin1,Jiang Hui1,Yan Surong1

Affiliation:

1. School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China

Abstract

With the in-depth application of artificial intelligence technology in the financial field, credit scoring models constructed by machine learning algorithms have become mainstream. However, the high-dimensional and complex attribute features of the borrower pose challenges to the predictive competence of the model. This paper proposes a hybrid model with a novel feature selection method and an enhanced voting method for credit scoring. First, a novel feature selection combined method based on a genetic algorithm (FSCM-GA) is proposed, in which different classifiers are used to select features in combination with a genetic algorithm and combine them to generate an optimal feature subset. Furthermore, an enhanced voting method (EVM) is proposed to integrate classifiers, with the aim of improving the classification results in which the prediction probability values are close to the threshold. Finally, the predictive competence of the proposed model was validated on three public datasets and five evaluation metrics (accuracy, AUC, F-score, Log loss and Brier score). The comparative experiment and significance test results confirmed the good performance and robustness of the proposed model.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference38 articles.

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