Minimax Classifier with Box Constraint on the Priors

Cyprien Gilet, Susana Barbosa, Lionel Fillatre
Proceedings of the Machine Learning for Health NeurIPS Workshop, PMLR 116:66-80, 2020.

Abstract

Learning a classifier in safety-critical applications like medicine raises several issues. Firstly, the class proportions, also called priors, are in general imbalanced or uncertain. Secondly, the classifier must consider some bounds on the priors taking the form of box constraints provided by experts. Thirdly, it is also necessary to consider any arbitrary loss function given by experts to evaluate the classification decision. Finally, the dataset may contain both categorical and numerical features. To deal with both categorical and numerical features, the numerical attributes are discretized. When considering only discrete features, we propose in this paper a box-constrained minimax classifier which addresses all the mentioned issues. We derive a projected subgradient algorithm to compute this classifier. The convergence of this algorithm is established. We finally perform experiments on the Framingham heart database for illustrating the relevance of our algorithm in health care field.

Cite this Paper

BibTeX
@InProceedings{pmlr-v116-gilet20a, title = {{Minimax Classifier with Box Constraint on the Priors}}, author = {Gilet, Cyprien and Barbosa, Susana and Fillatre, Lionel}, booktitle = {Proceedings of the Machine Learning for Health NeurIPS Workshop}, pages = {66--80}, year = {2020}, editor = {Dalca, Adrian V. and McDermott, Matthew B.A. and Alsentzer, Emily and Finlayson, Samuel G. and Oberst, Michael and Falck, Fabian and Beaulieu-Jones, Brett}, volume = {116}, series = {Proceedings of Machine Learning Research}, month = {13 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v116/gilet20a/gilet20a.pdf}, url = {https://proceedings.mlr.press/v116/gilet20a.html}, abstract = {Learning a classifier in safety-critical applications like medicine raises several issues. Firstly, the class proportions, also called priors, are in general imbalanced or uncertain. Secondly, the classifier must consider some bounds on the priors taking the form of box constraints provided by experts. Thirdly, it is also necessary to consider any arbitrary loss function given by experts to evaluate the classification decision. Finally, the dataset may contain both categorical and numerical features. To deal with both categorical and numerical features, the numerical attributes are discretized. When considering only discrete features, we propose in this paper a box-constrained minimax classifier which addresses all the mentioned issues. We derive a projected subgradient algorithm to compute this classifier. The convergence of this algorithm is established. We finally perform experiments on the Framingham heart database for illustrating the relevance of our algorithm in health care field.} }
Endnote
%0 Conference Paper %T Minimax Classifier with Box Constraint on the Priors %A Cyprien Gilet %A Susana Barbosa %A Lionel Fillatre %B Proceedings of the Machine Learning for Health NeurIPS Workshop %C Proceedings of Machine Learning Research %D 2020 %E Adrian V. Dalca %E Matthew B.A. McDermott %E Emily Alsentzer %E Samuel G. Finlayson %E Michael Oberst %E Fabian Falck %E Brett Beaulieu-Jones %F pmlr-v116-gilet20a %I PMLR %P 66--80 %U https://proceedings.mlr.press/v116/gilet20a.html %V 116 %X Learning a classifier in safety-critical applications like medicine raises several issues. Firstly, the class proportions, also called priors, are in general imbalanced or uncertain. Secondly, the classifier must consider some bounds on the priors taking the form of box constraints provided by experts. Thirdly, it is also necessary to consider any arbitrary loss function given by experts to evaluate the classification decision. Finally, the dataset may contain both categorical and numerical features. To deal with both categorical and numerical features, the numerical attributes are discretized. When considering only discrete features, we propose in this paper a box-constrained minimax classifier which addresses all the mentioned issues. We derive a projected subgradient algorithm to compute this classifier. The convergence of this algorithm is established. We finally perform experiments on the Framingham heart database for illustrating the relevance of our algorithm in health care field.
APA
Gilet, C., Barbosa, S. & Fillatre, L.. (2020). Minimax Classifier with Box Constraint on the Priors. Proceedings of the Machine Learning for Health NeurIPS Workshop, in Proceedings of Machine Learning Research 116:66-80 Available from https://proceedings.mlr.press/v116/gilet20a.html.

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