DEA based dimensionality reduction for classification problems satisfying strict non-satiety assumption |
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Authors: | Parag C Pendharkar Marvin D Troutt |
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Institution: | a School of Business Administration, Penn State Harrisburg, 777 West Harrisburg Pike, Middletown, PA 17057, United States b College of Business Administration, Kent State University, Kent, OH 44242, United States |
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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. |
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Keywords: | Data envelopment analysis Data mining Dimensionality reduction Discriminant analysis Goal programming |
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