
Interpretable Cascade Classifiers with Abstention
Clertant, Matthieu; Sokolovska, Nataliya; Chevaleyre, Yann; Hanczar, Blaise (2019), Interpretable Cascade Classifiers with Abstention, in Chaudhuri, Kamalika; Sugiyama, Masashi, 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019), Proceedings of Machine Learning Research, p. 89:2312-2320
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Type
Communication / ConférenceDate
2019Conference title
22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019)Conference date
2019-04Conference city
Naha, OkinawaConference country
JapanBook title
22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019)Book author
Chaudhuri, Kamalika; Sugiyama, MasashiPublisher
Proceedings of Machine Learning Research
Pages
89:2312-2320
Metadata
Show full item recordAuthor(s)
Clertant, MatthieuSokolovska, Nataliya
Chevaleyre, Yann
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Hanczar, Blaise
Informatique, Biologie Intégrative et Systèmes Complexes [IBISC]
Abstract (EN)
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.Subjects / Keywords
sequential decisionsRelated items
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