Interpretable Cascade Classifiers with Abstention

Matthieu Clertant, Nataliya Sokolovska, Yann Chevaleyre, Blaise Hanczar
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:2312-2320, 2019.

Abstract

In many prediction tasks such as medical diagnostics, sequential decisions are crucial to provide optimal individual treatment. Budget in real-life applications is always limited, and it can represent any limited resource such as time, money, or side effects of medications. In this contribution, we develop a POMDP-based framework to learn cost-sensitive heterogeneous cascading systems. We provide both the theoretical support for the introduced approach and the intuition behind it. We evaluate our novel method on some standard benchmarks, and we discuss how the learned models can be interpreted by human experts.

Cite this Paper

BibTeX
@InProceedings{pmlr-v89-clertant19a, title = {Interpretable Cascade Classifiers with Abstention}, author = {Clertant, Matthieu and Sokolovska, Nataliya and Chevaleyre, Yann and Hanczar, Blaise}, booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics}, pages = {2312--2320}, year = {2019}, editor = {Chaudhuri, Kamalika and Sugiyama, Masashi}, volume = {89}, series = {Proceedings of Machine Learning Research}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v89/clertant19a/clertant19a.pdf}, url = {https://proceedings.mlr.press/v89/clertant19a.html}, abstract = {In many prediction tasks such as medical diagnostics, sequential decisions are crucial to provide optimal individual treatment. Budget in real-life applications is always limited, and it can represent any limited resource such as time, money, or side effects of medications. In this contribution, we develop a POMDP-based framework to learn cost-sensitive heterogeneous cascading systems. We provide both the theoretical support for the introduced approach and the intuition behind it. We evaluate our novel method on some standard benchmarks, and we discuss how the learned models can be interpreted by human experts.} }
Endnote
%0 Conference Paper %T Interpretable Cascade Classifiers with Abstention %A Matthieu Clertant %A Nataliya Sokolovska %A Yann Chevaleyre %A Blaise Hanczar %B Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Masashi Sugiyama %F pmlr-v89-clertant19a %I PMLR %P 2312--2320 %U https://proceedings.mlr.press/v89/clertant19a.html %V 89 %X In many prediction tasks such as medical diagnostics, sequential decisions are crucial to provide optimal individual treatment. Budget in real-life applications is always limited, and it can represent any limited resource such as time, money, or side effects of medications. In this contribution, we develop a POMDP-based framework to learn cost-sensitive heterogeneous cascading systems. We provide both the theoretical support for the introduced approach and the intuition behind it. We evaluate our novel method on some standard benchmarks, and we discuss how the learned models can be interpreted by human experts.
APA
Clertant, M., Sokolovska, N., Chevaleyre, Y. & Hanczar, B.. (2019). Interpretable Cascade Classifiers with Abstention. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:2312-2320 Available from https://proceedings.mlr.press/v89/clertant19a.html.

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