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A potential use of data envelopment analysis for the inverse classification problem

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  • Pendharkar, Parag C.

Abstract

We propose a methodology that uses data envelopment analysis (DEA) for solving the inverse classification problem. An inverse classification problem involves finding out how predictor attributes of a case can be changed so that the case can be classified into a different and more desirable class. For a binary classification problem and non-negative decision-making attributes, we show that under the assumption of conditional monotonicity, and convexity of classes, DEA can be used for inverse classification problem. We illustrate the application of our proposed methodology on a hypothetical and a real-life bankruptcy prediction data.

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  • Pendharkar, Parag C., 2002. "A potential use of data envelopment analysis for the inverse classification problem," Omega, Elsevier, vol. 30(3), pages 243-248, June.
    • Handle: RePEc:eee:jomega:v:30:y:2002:i:3:p:243-248
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    References listed on IDEAS

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    1. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    2. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    3. Seiford, Lawrence M. & Zhu, Joe, 1998. "Stability regions for maintaining efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 108(1), pages 127-139, July.
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    Cited by:

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    3. Jie Sun, 2012. "Integration Of Random Sample Selection, Support Vector Machines And Ensembles For Financial Risk Forecasting With An Empirical Analysis On The Necessity Of Feature Selection," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(4), pages 229-246, October.
    4. Salwa Kessioui & Michalis Doumpos & Constantin Zopounidis, 2023. "A Bibliometric Overview of the State-of-the-Art in Bankruptcy Prediction Methods and Applications," World Scientific Book Chapters, in: Emilios Galariotis & Alexandros Garefalakis & Christos Lemonakis & Marios Menexiadis & Constantin Zo (ed.), Governance and Financial Performance Current Trends and Perspectives, chapter 6, pages 123-153, World Scientific Publishing Co. Pte. Ltd..
    5. T. R. Wang & N. Pedroni & E. Zio & V. Mousseau, 2020. "Identification of Protective Actions to Reduce the Vulnerability of Safety‐Critical Systems to Malevolent Intentional Acts: An Optimization‐Based Decision‐Making Approach," Risk Analysis, John Wiley & Sons, vol. 40(3), pages 565-587, March.
    6. Vincent Mousseau & Özgür Özpeynirci & Selin Özpeynirci, 2018. "Inverse multiple criteria sorting problem," Annals of Operations Research, Springer, vol. 267(1), pages 379-412, August.
    7. Ilke Aydogan & Loïc Berger & Vincent Theroude, 2024. "Pay all subjects or pay only some? An experiment on decision-making under risk and ambiguity," Working Papers 2024-iRisk-03, IESEG School of Management.
    8. Max Biggs & Rim Hariss & Georgia Perakis, 2023. "Constrained optimization of objective functions determined from random forests," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 397-415, February.
    9. Qianying Jin & Kristiaan Kerstens & Ignace Van de Woestyne, 2024. "Convex and nonconvex nonparametric frontier-based classification methods for anomaly detection," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(4), pages 1213-1239, December.

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