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.
Year of publication: |
2011
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Authors: | Pendharkar, Parag C. ; Troutt, Marvin D. |
Published in: |
European Journal of Operational Research. - Elsevier, ISSN 0377-2217. - Vol. 212.2011, 1, p. 155-163
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Publisher: |
Elsevier |
Keywords: | Data envelopment analysis Data mining Dimensionality reduction Discriminant analysis Goal programming |
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