Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4959648 | European Journal of Operational Research | 2017 | 28 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Sebastián Maldonado, Juan Pérez, Cristián Bravo,