In this paper, we propose a profit-driven approach for classifier construction and simultaneous variable selection based on linear Support Vector Machines. The main goal is to incorporate business-related information such as the variable acquisition costs, the Types I and II error costs, and the profit generated by correctly classified instances, into the modeling process. Our proposal incorporates a group penalty function in the SVM formulation in order to penalize the variables simultaneously that belong to the same group, assuming that companies often acquire groups of related variables for a given cost rather than acquiring them individually. The proposed framework was studied in a credit scoring problem for a Chilean bank, and led to superior performance with respect to business-related goals.
Bibliographical noteFunding Information:
The first author was supported by FONDECYT project 1160738 . The third author was funded by FONDECYT project 1160894 . The fourth author was supported by FONDECYT project 11160320 . This research was partially funded by the Complex Engineering Systems Institute , ISCI (ICM-FIC: P05-004-F , CONICYT : FB0816 ).
© 2017 Elsevier B.V.
- Credit scoring
- Group penalty
- Profit measure
- Support Vector Machines