Name
Title | ||
|---|---|---|
Integrated framework for profit-based feature selection and SVM classification in credit scoring. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.dss.2017.10.007 | Decision Support Systems |
Keywords | Field | DocType |
Profit measure,Group penalty,Credit scoring,Support Vector Machines,Analytics | Data mining,Feature selection,Computer science,Support vector machine,Artificial intelligence,Classifier (linguistics),Machine learning,Penalty method | Journal |
Volume | ISSN | Citations |
104 | 0167-9236 | 6 |
PageRank | References | Authors |
0.39 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sebastián Maldonado | 1 | 508 | 32.45 |
| Cristián Bravo | 2 | 124 | 10.63 |
| Julio López | 3 | 124 | 13.49 |
| Juan F. Pérez | 4 | 106 | 11.80 |