Yang Li, Jingxiang Li, Yuanping Wang

【Abstract】

In this study, we propose an AUC(area under the ROC curve) regression with the MCP(the Minimax Conacave Penalty)regularization(MCP-AUCR) to deal with the forecasting and feature selection issues for the imbalanced data. The proposed method can solve the imbalanced issues for the optimization of AUC based target and has a good performance on the feature selection. We discuss the idea of the MCP-AUCR and an iterative coordinate descent algorithm. Numerical studies are conducted to show the good property of the proposed method. And an applied study of the financial early warning system for Chinese listed corporations is analyzed as an illustrative example.

【Keywords】

AUC regression, MCP penalty, feature selection, financial early warning system