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

Parag C. Pendharkar, Marvin D. Troutt

Research output: Contribution to journalArticle

5 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)155-163
Number of pages9
JournalEuropean Journal of Operational Research
Volume212
Issue number1
DOIs
StatePublished - Jul 1 2011

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

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