Integrated framework for profit-based feature selection and SVM classification in credit scoring

Sebastián Maldonado, Cristián Bravo, Julio López, Juan Eduardo Pérez

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

37 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Páginas (desde-hasta)113-121
Número de páginas9
PublicaciónDecision Support Systems
Volumen104
DOI
EstadoPublicada - dic 2017

Nota bibliográfica

Publisher Copyright:
© 2017 Elsevier B.V.

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