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

Parag C. Pendharkar and Marvin D. Troutt

European Journal of Operational Research, 2011, vol. 212, issue 1, 155-163

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.

Keywords: Data; envelopment; analysis; Data; mining; Dimensionality; reduction; Discriminant; analysis; Goal; programming (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:212:y:2011:i:1:p:155-163

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