Code and Data Repository for Feature Selection and Grouping Effect Analysis for Credit Evaluation via Regularized Diagonal Distance Metric Learning
该仓库实现了正则化对角距离度量学习模型，通过特征缩放改进距离度量、选择特征并分析分组效应，适用于大规模金融数据信用评估，不追求线性可分性并考虑特征相关性。
This repository provides an implementation of the regularized diagonal Distance Metric Learning (DML) model, which improves distance metrics, selects features, and conducts grouping effect analysis by rescaling the features. One characteristic of the proposed model is that it does not pursue linear separability, which is highly unrealistic in financial data. Another characteristic of the proposed model is that it considers correlated features when conducting feature selection, and thus, does not neglect important credit risk sources when used for credit evaluation. The implementation of the solver based on the Alternating Direction Method of Multipliers(ADMM) makes it suitable for large-scale financial applications. The repository also provides the scripts, data, and experimental results reported in the paper. This repository includes four folders, src, scripts, data, and results.