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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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    1. A. Duarte Silva & Antonie Stam, 1997. "A mixed integer programming algorithm for minimizing the training sample misclassification cost in two-group classification," Annals of Operations Research, Springer, vol. 74(0), pages 129-157, November.
    2. Robert Pavur & Pradit Wanarat & Constantine Loucopoulos, 1997. "Examination of the classificatory performance of MIP models with secondary goals for the two-group discriminant problem," Annals of Operations Research, Springer, vol. 74(0), pages 173-189, November.
    3. Zhimin Huang & Susan Li, 2001. "Stochastic DEA Models With Different Types of Input-Output Disturbances," Journal of Productivity Analysis, Springer, vol. 15(2), pages 95-113, March.
    4. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    5. Ognian Asparoukhov & Antonie Stam, 1997. "Mathematical programming formulations for two-group classification with binary variables," Annals of Operations Research, Springer, vol. 74(0), pages 89-112, November.
    6. Richard Gallagher & Eva Lee & David Patterson, 1997. "Constrained discriminant analysis via 0/1 mixed integer programming," Annals of Operations Research, Springer, vol. 74(0), pages 65-88, November.
    7. Marvin D. Troutt, 1995. "A Maximum Decisional Efficiency Estimation Principle," Management Science, INFORMS, vol. 41(1), pages 76-82, January.
    8. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    9. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    10. Paul Rubin, 1997. "Solving mixed integer classification problems by decomposition," Annals of Operations Research, Springer, vol. 74(0), pages 51-64, November.
    11. Anil Gaba & W. Kip Viscusi, 1998. "Differences in Subjective Risk Thresholds: Worker Groups as an Example," Management Science, INFORMS, vol. 44(6), pages 801-811, June.
    12. Marvin D. Troutt, 1994. "Direction-Specific Gradient Scaling for Interactive Multicriterion Optimization Using an Abstract Mass Concept," Operations Research, INFORMS, vol. 42(6), pages 1110-1119, December.
    13. A. M. Geoffrion & J. S. Dyer & A. Feinberg, 1972. "An Interactive Approach for Multi-Criterion Optimization, with an Application to the Operation of an Academic Department," Management Science, INFORMS, vol. 19(4-Part-1), pages 357-368, December.
    14. Fred M. Feinberg & Joel Huber, 1996. "A Theory of Cutoff Formation Under Imperfect Information," Management Science, INFORMS, vol. 42(1), pages 65-84, January.
    15. 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.
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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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