Thumbnail

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

View/Open
Type
Communication / Conférence
Date
2019
Conference title
22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019)
Conference date
2019-04
Conference city
Naha, Okinawa
Conference country
Japan
Book title
22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019)
Book author
Chaudhuri, Kamalika; Sugiyama, Masashi
Publisher
Proceedings of Machine Learning Research
Pages
89:2312-2320
Metadata
Show full item record
Author(s)
Clertant, Matthieu

Sokolovska, 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 decisions