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Robust classification for skewed data

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Abstract

In this paper we propose a robust classification rule for skewed unimodal distributions. For low dimensional data, the classifier is based on minimizing the adjusted outlyingness to each group. In the case of high dimensional data, the robustified SIMCA method is adjusted for skewness. The robustness of the methods is investigated through different simulations and by applying it to some datasets.

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Authors and Affiliations

  1. Department of Mathematics, LStat, Katholieke Universiteit Leuven, Celestijnenlaan 200B, 3001, Heverlee, Belgium

    Mia Hubert & Stephan Van der Veeken

Authors
  1. Mia Hubert
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  2. Stephan Van der Veeken
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Corresponding author

Correspondence to Mia Hubert.

Additional information

We acknowledge financial support by the GOA/07/04-project of the Research Fund K.U.Leuven and by the IAP research network no. P6/03 of the Federal Science Policy, Belgium.

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Hubert, M., Van der Veeken, S. Robust classification for skewed data. Adv Data Anal Classif 4, 239–254 (2010). https://doi.org/10.1007/s11634-010-0066-3

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  • DOI: https://doi.org/10.1007/s11634-010-0066-3

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