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

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  • Pendharkar, Parag C.
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    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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    Bibliographic Info

    Article provided by Elsevier in its journal Omega.

    Volume (Year): 30 (2002)
    Issue (Month): 3 (June)
    Pages: 243-248

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    Handle: RePEc:eee:jomega:v:30:y:2002:i:3:p:243-248

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    Keywords: Classification Data envelopment analysis Linear programming Discriminant analysis;

    References

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    1. 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.
    2. 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.
    3. 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.
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