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Classification accuracy in discriminant analysis: a mixed integer programming approach

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  • J J Glen

    (University of Edinburgh)

Abstract

Classification models can be developed by statistical or mathematical programming discriminant analysis techniques. Variable selection extensions of these techniques allow the development of classification models with a limited number of variables. Although stepwise statistical variable selection methods are widely used, the performance of the resultant classification models may not be optimal because of the stepwise selection protocol and the nature of the group separation criterion. A mixed integer programming approach for selecting variables for maximum classification accuracy is developed in this paper and the performance of this approach, measured by the leave-one-out hit rate, is compared with the published results from a statistical approach in which all possible variable subsets were considered. Although this mixed integer programming approach can only be applied to problems with a relatively small number of observations, it may be of great value where classification decisions must be based on a limited number of observations.

Suggested Citation

  • J J Glen, 2001. "Classification accuracy in discriminant analysis: a mixed integer programming approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(3), pages 328-339, March.
    • Handle: RePEc:pal:jorsoc:v:52:y:2001:i:3:d:10.1057_palgrave.jors.2601085
    DOI: 10.1057/palgrave.jors.2601085
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    Citations

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    Cited by:

    1. Michael O. Olusola & Sydney I. Onyeagu, 2020. "On the binary classification problem in discriminant analysis using linear programming methods," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 30(1), pages 119-130.
    2. Hung-Tso Lin & Tsung-Yu Chou & Yen-Ting Chen & Yin-Chi Huang, 2014. "Profitability analysis using IDEA–DA framework," Annals of Operations Research, Springer, vol. 223(1), pages 291-308, December.
    3. Soulef Smaoui & Belaid Aouni, 2017. "Fuzzy goal programming model for classification problems," Annals of Operations Research, Springer, vol. 251(1), pages 141-160, April.
    4. Mingue Sun, 2009. "Liquidity Risk and Financial Competition: A Mixed Integer Programming Model for Multiple-Class Discriminant Analysis," Working Papers 0102, College of Business, University of Texas at San Antonio.
    5. J J Glen, 2008. "An additive utility mixed integer programming model for nonlinear discriminant analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(11), pages 1492-1505, November.
    6. J J Glen, 2005. "Mathematical programming models for piecewise-linear discriminant analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(3), pages 331-341, March.
    7. Glen, J.J., 2006. "A comparison of standard and two-stage mathematical programming discriminant analysis methods," European Journal of Operational Research, Elsevier, vol. 171(2), pages 496-515, June.
    8. T. Nguyen D. & T. Do T. & B. Nguyen N. & Т. Нгуен Д. & Т. До Т. & Б. Нгуен Н., 2016. "Применение дискриминационной модели в управлении риском потребительских кредитов в коммерческом банке Вьетнама // Applying Discriminant Model to Manage Credit Risk for Consumer Loans in Vietnamese Com," Review of Business and Economics Studies // Review of Business and Economics Studies, Финансовый Университет // Financial University, vol. 4(4), pages 5-16.
    9. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    10. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    11. J. J. Glen, 2004. "Dichotomous categorical variable formation in mathematical programming discriminant analysis models," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(4), pages 575-596, June.
    12. Mingue Sun, 2009. "Liquidity Risk and Financial Competition: A Mixed Integer Programming Model for Multiple-Class Discriminant Analysis," Working Papers 0102, College of Business, University of Texas at San Antonio.
    13. Sueyoshi, Toshiyuki, 2004. "Mixed integer programming approach of extended DEA-discriminant analysis," European Journal of Operational Research, Elsevier, vol. 152(1), pages 45-55, January.
    14. K Falangis & J J Glen, 2010. "Heuristics for feature selection in mathematical programming discriminant analysis models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(5), pages 804-812, May.

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