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A Bayesian framework for the combination of classifier outputs

Author

Listed:
  • H Zhu

    (Fair, Issac and Company)

  • P A Beling

    (University of Virginia)

  • G A Overstreet

    (University of Virginia)

Abstract

We explore a Bayesian framework for constructing combinations of classifier outputs, as a means to improving overall classification results. We propose a sequential Bayesian framework to estimate the posterior probability of being in a certain class given multiple classifiers. This framework, which employs meta-Gaussian modelling but makes no assumptions about the distribution of classifier outputs, allows us to capture nonlinear dependencies between the combined classifiers and individuals. An important property of our method is that it produces a combined classifier that dominates the individuals upon which it is based in terms of Bayes risk, error rate, and receiver operating characteristic (ROC) curve. To illustrate the method, we show empirical results from the combination of credit scores generated from four different scoring models.

Suggested Citation

  • H Zhu & P A Beling & G A Overstreet, 2002. "A Bayesian framework for the combination of classifier outputs," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(7), pages 719-727, July.
  • Handle: RePEc:pal:jorsoc:v:53:y:2002:i:7:d:10.1057_palgrave.jors.2601262
    DOI: 10.1057/palgrave.jors.2601262
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    Citations

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    Cited by:

    1. K Rajaratnam & P Beling & G Overstreet, 2010. "Scoring decisions in the context of economic uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 421-429, March.
    2. J Whittaker & C Whitehead & M Somers, 2007. "A dynamic scorecard for monitoring baseline performance with application to tracking a mortgage portfolio," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(7), pages 911-921, July.
    3. Lu Gao & Kanshukan Rajaratnam & Peter Beling, 2016. "Loan origination decisions using a multinomial scorecard," Annals of Operations Research, Springer, vol. 243(1), pages 199-210, August.
    4. Andrew R. Sanderford & George A. Overstreet & Peter A. Beling & Kanshukan Rajaratnam, 2015. "Energy-efficient homes and mortgage risk: crossing the chasm at last?," Environment Systems and Decisions, Springer, vol. 35(1), pages 157-168, March.
    5. Finlay, Steven, 2011. "Multiple classifier architectures and their application to credit risk assessment," European Journal of Operational Research, Elsevier, vol. 210(2), pages 368-378, April.
    6. Kanshukan Rajaratnam & Peter Beling & George Overstreet, 2017. "Regulatory capital decisions in the Context of consumer loan portfolios," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(7), pages 847-858, July.
    7. Zhiqiang Zheng & Balaji Padmanabhan, 2007. "Constructing Ensembles from Data Envelopment Analysis," INFORMS Journal on Computing, INFORMS, vol. 19(4), pages 486-496, November.
    8. P Beling & Z Covaliu & R M Oliver, 2005. "Optimal scoring cutoff policies and efficient frontiers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1016-1029, September.

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