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.

互联网融资是个人或中小企业使用在线交易平台借钱的银行的重要替代方案。互联网融资的中心问题是如何确定与信用风险密切相关的最有影响力的因素。这个问题是固有的挑战，因为互联网融资的原始数据通常与复杂的结构关联相关联，并且通常包含许多不相关和多余的特征。为了有效地确定互联网融资中信用风险评估的最显着特征，我们开发了一种新的多结构相互作用弹性网模型用于特征选择（MSIEN）。我们的想法基于将原始矢量特征转换为基于结构的特征图表示形式，以封装成对样本之间的结构关系，并定义了两个新的信息理论标准。一个准则使不同的成对特征组合相对于目标特征图的联合相关性最大化，而另一个准则使成对特征之间的冗余度最小。然后，获得了两个结构相互作用矩阵，其中的元素代表了所提出的信息理论测度。为了确定最有用的特征，我们针对特征子集选择问题制定了一个新的优化模型，该模型结合了交互矩阵和弹性网正则化模型。我们利用一种有效的迭代优化算法来解决所提出的问题，并提供其收敛性和计算复杂度的理论分析。最后，