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

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.