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arXiv · 2204.13111

Open challenges for Machine Learning based Early Decision-Making research

Alexis Bondu·Youssef Achenchabe·Albert Bifet·Fabrice Clérot·Antoine Cornuéjols·Joao Gama·Georges Hébrail·Vincent Lemaire·Pierre-François Marteau
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Abstract

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.

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Alexis Bondu, Youssef Achenchabe, Albert Bifet, Fabrice Clérot, Antoine Cornuéjols, Joao Gama, Georges Hébrail, Vincent Lemaire, Pierre-François Marteau. 2022-04-27. Open challenges for Machine Learning based Early Decision-Making research. https://doi.org/10.1145/3575637.3575643

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