Robust Cost Sensitive Support Vector Machine

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Shuichi Katsumata, Akiko Takeda ;
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, PMLR 38:434-443, 2015.

Abstract

In this paper we consider robust classifications and show equivalence between the regularized classifications. In general, robust classifications are used to create a classifier robust to data by taking into account the uncertainty of the data. Our result shows that regularized classifications inherit robustness and provide reason on why some regularized classifications tend to be robust against data. Although most robust classification problems assume that every uncertain data lie within an identical bounded set, this paper considers a generalized model where the sizes of the bounded sets are different for each data. These models can be transformed into regularized classification models where the penalties for each data are assigned according to their losses. We see that considering such models opens up for new applications. For an example, we show that this robust classification technique can be used for Imbalanced Data Learning. We conducted experimentation with actual data and compared it with other IDL algorithms such as Cost Sensitive SVMs. This is a novel usage for the robust classification scheme and encourages it to be a suitable candidate for imbalanced data learning.

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