DEA based dimensionality reduction for classification problems satisfying strict non-satiety assumption

This study shows how data envelopment analysis (DEA) can be used to reduce vertical dimensionality of certain data mining databases. The study illustrates basic concepts using a real-world graduate admissions decision task. It is well known that cost sensitive mixed integer programming (MIP) problems are NP-complete. This study shows that heuristic solutions for cost sensitive classification problems can be obtained by solving a simple goal programming problem by that reduces the vertical dimension of the original learning dataset. Using simulated datasets and a misclassification cost performance metric, the performance of proposed goal programming heuristic is compared with the extended DEA-discriminant analysis MIP approach. The holdout sample results of our experiments shows that the proposed heuristic approach outperforms the extended DEA-discriminant analysis MIP approach.