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A simple method for combining estimates to improve the overall error rates in classification

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  • Narayanaswamy Balakrishnan
  • Majid Mojirsheibani

Abstract

We present a new and easy-to-implement procedure for combining $$J\ge 2$$ J ≥ 2 different classifiers in order to develop more effective classification rules. The method works by finding nonparametric estimates of the class conditional expectation of a new observation (that has to be classified), conditional on the vector of $$J$$ J predicted values corresponding to the $$J$$ J individual classifiers. Here, we propose a data-splitting method to carry out the estimation of various class conditional expectations. It turns out that, under rather minimal assumptions, the proposed combined classifier is optimal in the sense that its overall misclassification error rate is asymptotically less than (or equal to) that of any one of the individual classifiers. Simulation studies are also carried out to evaluate the proposed method. Furthermore, to make the numerical results more challenging, we also consider stable distributions (Cauchy) with rather high dimensions. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Narayanaswamy Balakrishnan & Majid Mojirsheibani, 2015. "A simple method for combining estimates to improve the overall error rates in classification," Computational Statistics, Springer, vol. 30(4), pages 1033-1049, December.
    • Handle: RePEc:spr:compst:v:30:y:2015:i:4:p:1033-1049
    DOI: 10.1007/s00180-015-0571-0
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    References listed on IDEAS

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    Cited by:

    1. Mojirsheibani, Majid & Kong, Jiajie, 2016. "An asymptotically optimal kernel combined classifier," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 91-100.

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