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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| creator | Bondu, Alexis Youssef Achenchabe Bifet, Albert Clérot, Fabrice Cornuéjols, Antoine Gama, Joao Hébrail, Georges Lemaire, Vincent Pierre-François Marteau |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.2204.13111 |
| format | Article |
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| title | Open challenges for Machine Learning based Early Decision-Making research |
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