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Article Dans Une Revue SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Année : 2022

Open challenges for Machine Learning based Early Decision-Making research

Résumé

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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Dates et versions

hal-03983223 , version 1 (14-09-2023)

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Citer

Alexis Bondu, Youssef Achenchabe, Albert Bifet, Fabrice Clerot, Antoine Cornuéjols, et al.. Open challenges for Machine Learning based Early Decision-Making research. SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining, 2022, 24 (2), pp.12-31. ⟨10.1145/3575637.3575643⟩. ⟨hal-03983223⟩
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