4.7 Article

Internet financing credit risk evaluation using multiple structural interacting elastic net feature selection

期刊

PATTERN RECOGNITION
卷 114, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2021.107835

关键词

Credit risk; Feature selection; Elastic net; Sparse learning; Structural interaction; Internet financing

资金

  1. National Natural Science Foundation of China [61602535, 61976235, 71972194, 61773415]
  2. Program for Innovation Research in Central University of Finance and Economics
  3. Youth Talent Development Support Program by Central University of Finance and Economics [QYP1908]

向作者/读者索取更多资源

The article introduces a new method MSIEN for feature selection in the field of internet finance to evaluate credit risk, identifying the most important features through building a structural interacting elastic net model.
Internet financing is an important alternative to banks where individuals or SMEs borrow money using online trading platforms. A central problem for internet financing is how to identify the most influential factors that are closely related to the credit risks. This problem is inherently challenging because the raw data of internet financing is often associated with complex structural correlations and usually contains many irrelevant and redundant features. To effectively identify the most salient features for credit risk evaluation in internet financing, we develop a new multiple structural interacting elastic net model for feature selection (MSIEN). Our idea is based on converting the original vectorial features into structure based feature graph representations to encapsulate structural relationship between pairwise samples, and defining two new information theoretic criteria. One criterion maximizes joint relevance of different pair wise feature combinations in relation to the target feature graph and the other minimizes the redundancy between pairwise features. Then two structural interaction matrices are obtained with the elements representing the proposed information theoretic measures. To identify the most informative features, we formulate a new optimization model which combines the interaction matrices and an elastic net regularization model for the feature subset selection problem. We exploit an efficient iterative optimization algorithm to solve the proposed problem and also provide the theoretical analyses on its convergence property and computational complexity. Finally, experimental results on datasets of internet financing demonstrate the effectiveness of the proposed MSIEN method. (c) 2021 Elsevier Ltd. All rights reserved.

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