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

Parag C. Pendharkar, Marvin D. Troutt

Research output: Contribution to journalArticle

5 Citations (Scopus)

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

Fingerprint

Data envelopment analysis
Mixed Integer Programming
Integer programming
Data Envelopment Analysis
Dimensionality Reduction
Classification Problems
Goal Programming
Discriminant analysis
Heuristics
Discriminant Analysis
Costs
Heuristic programming
Vertical
Misclassification
Performance Metrics
Dimensionality
Data mining
Computational complexity
Data Mining
NP-complete problem

All Science Journal Classification (ASJC) codes

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

Cite this

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DEA based dimensionality reduction for classification problems satisfying strict non-satiety assumption. / Pendharkar, Parag C.; Troutt, Marvin D.

In: European Journal of Operational Research, Vol. 212, No. 1, 01.07.2011, p. 155-163.

Research output: Contribution to journalArticle

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