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Interactive classification using data envelopment analysis

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  • Parag Pendharkar
  • Marvin Troutt

Abstract

In this paper, we illustrate how data envelopment analysis (DEA) can be used to aid interactive classification. We assume that the scoring function for the classification problem is known. We use DEA to identify difficult to classify cases from a database and present them to the decision-maker one at a time. The decision-maker assigns a class to the presented case and based on the decision-maker class assignment, a tradeoff cutting plane is drawn using the scoring function and decision-maker’s input. The procedure continues for finite number of iterations and terminates with the final discriminant function. We also show how a hybrid DEA and mathematical programming approach can be used when user interaction is not desired. For non-interactive case, we compare a hybrid DEA and mathematical programming based approach with several statistical and machine learning approaches, and show that the hybrid approach provides competitive performance when compared to the other machine learning approaches. Copyright Springer Science+Business Media, LLC 2014

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  • Parag Pendharkar & Marvin Troutt, 2014. "Interactive classification using data envelopment analysis," Annals of Operations Research, Springer, vol. 214(1), pages 125-141, March.
    • Handle: RePEc:spr:annopr:v:214:y:2014:i:1:p:125-141:10.1007/s10479-012-1091-8
    DOI: 10.1007/s10479-012-1091-8
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    2. Valero-Carreras, Daniel & Aparicio, Juan & Guerrero, Nadia M., 2021. "Support vector frontiers: A new approach for estimating production functions through support vector machines," Omega, Elsevier, vol. 104(C).

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