Robust Cost Sensitive Support Vector Machine

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v38-katsumata15, title = {{Robust Cost Sensitive Support Vector Machine}}, author = {Katsumata, Shuichi and Takeda, Akiko}, booktitle = {Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics}, pages = {434--443}, year = {2015}, editor = {Lebanon, Guy and Vishwanathan, S. V. N.}, volume = {38}, series = {Proceedings of Machine Learning Research}, address = {San Diego, California, USA}, month = {09--12 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v38/katsumata15.pdf}, url = {https://proceedings.mlr.press/v38/katsumata15.html}, 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.} }
Endnote
%0 Conference Paper %T Robust Cost Sensitive Support Vector Machine %A Shuichi Katsumata %A Akiko Takeda %B Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2015 %E Guy Lebanon %E S. V. N. Vishwanathan %F pmlr-v38-katsumata15 %I PMLR %P 434--443 %U https://proceedings.mlr.press/v38/katsumata15.html %V 38 %X 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.
RIS
TY - CPAPER TI - Robust Cost Sensitive Support Vector Machine AU - Shuichi Katsumata AU - Akiko Takeda BT - Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics DA - 2015/02/21 ED - Guy Lebanon ED - S. V. N. Vishwanathan ID - pmlr-v38-katsumata15 PB - PMLR DP - Proceedings of Machine Learning Research VL - 38 SP - 434 EP - 443 L1 - http://proceedings.mlr.press/v38/katsumata15.pdf UR - https://proceedings.mlr.press/v38/katsumata15.html AB - 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. ER -
APA
Katsumata, S. & Takeda, A.. (2015). Robust Cost Sensitive Support Vector Machine. Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 38:434-443 Available from https://proceedings.mlr.press/v38/katsumata15.html.

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