Open challenges for Machine Learning based Early Decision-Making research
Published in ACM SIGKDD, 2022
Recommended citation: Bondu, A., Achenchabe, Y., Bifet, A., Clérot, F., Cornuéjols, A., Gama, J., ... & Marteau, P. F. (2022). Open challenges for machine learning based early decision-making research. ACM SIGKDD Explorations Newsletter, 24(2), 12-31. https://dl.acm.org/doi/abs/10.1145/3575637.3575643
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.