Code and Data Repository for Feature Selection and Grouping Effect Analysis for Credit Evaluation via Regularized Diagonal Distance Metric Learning

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.