Open challenges for Machine Learning based Early Decision-Making research

More and more applications require early decisions, i.e. taken as soon as possible from partially observed data. However, the later a decision is made, the more its accuracy tends to improve, since the description of the problem to hand is enriched over time. Such a compromise between the earliness...

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Bibliographic Details
Published in:arXiv.org 2022-05
Main Authors: Bondu, Alexis, Youssef Achenchabe, Bifet, Albert, Clérot, Fabrice, Cornuéjols, Antoine, Gama, Joao, Hébrail, Georges, Lemaire, Vincent, Pierre-François Marteau
Format: Article
Language:English
Subjects:
Decision making
Machine learning
Online Access:Get full text
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Summary:More and more applications require early decisions, i.e. taken as soon as possible from partially observed data. However, the later a decision is made, the more its accuracy tends to improve, since the description of the problem to hand is enriched over time. Such a compromise between the earliness and the accuracy of decisions has been particularly studied in the field of Early Time Series Classification. This paper introduces a more general problem, called Machine Learning based Early Decision Making (ML-EDM), which consists in optimizing the decision times of models in a wide range of settings where data is collected over time. After defining the ML-EDM problem, ten challenges are identified and proposed to the scientific community to further research in this area. These challenges open important application perspectives, discussed in this paper.
ISSN:2331-8422
DOI:10.48550/arxiv.2204.13111