A decision tree-based method for selection of input-output factors in DEA

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4 Scopus citations

Abstract

We propose a method for selection of input-output factors in DEA. It is designed to select better combinations of input-output factors that are well suited for evaluating substantial performance of DMUs. Several selected DEA models with different combinations of input-output factors are evaluated, and the relationship between the computed efficiency scores and a single performance criterion of DMUs is investigated using decision tree. Based on the results of decision tree analysis, a relatively better DEA model can be chosen, which is expected to effectively assess the true performance of DMUs. We illustrate the effectiveness of the proposed method by applying it to the efficiency evaluation of 101 companies in steel and metal industry listed on the Korean stock market.

Original languageEnglish
Title of host publicationProceedings of the 2008 International Conference on Artificial Intelligence, ICAI 2008 and Proceedings of the 2008 International Conference on Machine Learning; Models, Technologies and Applications
Pages762-769
Number of pages8
StatePublished - 2008
Event2008 International Conference on Artificial Intelligence, ICAI 2008 and 2008 International Conference on Machine Learning; Models, Technologies and Applications, MLMTA 2008 - Las Vegas, NV, United States
Duration: 14 Jul 200817 Jul 2008

Publication series

NameProceedings of the 2008 International Conference on Artificial Intelligence, ICAI 2008 and Proceedings of the 2008 International Conference on Machine Learning; Models, Technologies and Applications

Conference

Conference2008 International Conference on Artificial Intelligence, ICAI 2008 and 2008 International Conference on Machine Learning; Models, Technologies and Applications, MLMTA 2008
Country/TerritoryUnited States
CityLas Vegas, NV
Period14/07/0817/07/08

Keywords

  • Benchmarking
  • Data envelopment analysis
  • Decision trees
  • Non-parametric methods
  • Statistical learning

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