Cost-based feature selection for Support Vector Machines: An application in credit scoring

Sebastián Maldonado, Juan Eduardo Pérez, Cristián Bravo

Research output: Contribution to journalArticlepeer-review

61 Scopus citations

Abstract

In this work we propose two formulations based on Support Vector Machines for simultaneous classification and feature selection that explicitly incorporate attribute acquisition costs. This is a challenging task for two main reasons: the estimation of the acquisition costs is not straightforward and may depend on multivariate factors, and the inter-dependence between variables must be taken into account for the modelling process since companies usually acquire groups of related variables rather than acquiring them individually. Mixed-integer linear programming models are proposed for constructing classifiers that constrain acquisition costs while classifying adequately. Experimental results using credit scoring datasets demonstrate the effectiveness of our methods in terms of predictive performance at a low cost compared to well-known feature selection approaches.

Original languageEnglish
Pages (from-to)656-665
Number of pages10
JournalEuropean Journal of Operational Research
Volume261
Issue number2
DOIs
StatePublished - 1 Sep 2017

Bibliographical note

Funding Information:
The first author was supported by FONDECYT project 1160738. The second author was funded by FONDECYT project 11160320. This research was partially funded by the Complex Engineering Systems Institute, ISCI (ICM-FIC: P05-004-F, CONICYT: FB0816). The authors are grateful to the anonymous reviewers, who contributed to improving the quality of the paper.

Publisher Copyright:
© 2017 Elsevier B.V.

Keywords

  • Analytics
  • Credit scoring
  • Feature selection
  • Mixed-integer programming
  • Support Vector Machines

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