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An Almost Surely Optimal Combined Classification Rule

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  • Mojirsheibani, Majid

Abstract

We propose a data-based procedure for combining a number of individual classifiers in order to construct more effective classification rules. Under some regularity conditions, the resulting combined classifier turns out to be almost surely superior to each of the individual classifiers. Here, superiority means lower misclassification error rate.

Suggested Citation

  • Mojirsheibani, Majid, 2002. "An Almost Surely Optimal Combined Classification Rule," Journal of Multivariate Analysis, Elsevier, vol. 81(1), pages 28-46, April.
    • Handle: RePEc:eee:jmvana:v:81:y:2002:i:1:p:28-46
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    References listed on IDEAS

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    1. Marta Horvath & Gábor Lugosi, 1996. "A data-dependent skeleton estimate and a scale-sensitive dimension for classification," Economics Working Papers 199, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Mojirsheibani, M., 1997. "A consistent combined classification rule," Statistics & Probability Letters, Elsevier, vol. 36(1), pages 43-47, November.
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    Cited by:

    1. Cholaquidis, Alejandro & Fraiman, Ricardo & Kalemkerian, Juan & Llop, Pamela, 2016. "A nonlinear aggregation type classifier," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 269-281.
    2. Hothorn, Torsten & Lausen, Berthold, 2005. "Bundling classifiers by bagging trees," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1068-1078, June.

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