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

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 pairwise 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.