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.
@article{arxiv.2204.13111,
title = {Open challenges for Machine Learning based Early Decision-Making research},
author = {Alexis Bondu and Youssef Achenchabe and Albert Bifet and Fabrice Clérot and Antoine Cornuéjols and Joao Gama and Georges Hébrail and Vincent Lemaire and Pierre-François Marteau},
journal= {arXiv preprint arXiv:2204.13111},
year = {2022}
}