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

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  • Pendharkar, Parag C.
  • Troutt, Marvin D.

Abstract

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.

Suggested Citation

  • Pendharkar, Parag C. & Troutt, Marvin D., 2011. "DEA based dimensionality reduction for classification problems satisfying strict non-satiety assumption," European Journal of Operational Research, Elsevier, vol. 212(1), pages 155-163, July.
    • Handle: RePEc:eee:ejores:v:212:y:2011:i:1:p:155-163
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    References listed on IDEAS

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    Cited by:

    1. Quanling Wei & Tsung-Sheng Chang & Song Han, 2014. "Quantile–DEA classifiers with interval data," Annals of Operations Research, Springer, vol. 217(1), pages 535-563, June.

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