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High dimensional classifiers in the imbalanced case

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  • Bak, Britta Anker
  • Jensen, Jens Ledet

Abstract

A binary classification problem is imbalanced when the number of samples from the two groups differs. For the high dimensional case, where the number of variables is much larger than the number of samples, imbalance leads to a bias in the classification. The independence classifier is studied theoretically and based on the analysis two new classifiers are suggested that can handle any imbalance ratio. The analytical results are supplemented by a simulation study, where the suggested classifiers in some aspects outperform multiple undersampling. For correlated data the ROAD classifier is considered and a suggestion is given for how to modify the classifier to handle the bias from imbalanced group sizes.

Suggested Citation

  • Bak, Britta Anker & Jensen, Jens Ledet, 2016. "High dimensional classifiers in the imbalanced case," Computational Statistics & Data Analysis, Elsevier, vol. 98(C), pages 46-59.
    • Handle: RePEc:eee:csdana:v:98:y:2016:i:c:p:46-59
    DOI: 10.1016/j.csda.2015.12.009
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    References listed on IDEAS

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    1. Britta Anker Bak & Jens Ledet Jensen & Morten Fenger-Grøn, 2015. "Classification Error of the Thresholded Independence Rule," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 32-42, March.
    2. Jianqing Fan & Yang Feng & Xin Tong, 2012. "A road to classification in high dimensional space: the regularized optimal affine discriminant," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(4), pages 745-771, September.
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    Cited by:

    1. Abpeykar, Shadi & Ghatee, Mehdi & Zare, Hadi, 2019. "Ensemble decision forest of RBF networks via hybrid feature clustering approach for high-dimensional data classification," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 12-36.

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