Open challenges for Machine Learning based Early Decision-Making research
This work addresses the trade-off between decision earliness and accuracy for applications requiring timely decisions from partial data, proposing a framework to advance research in early decision-making across various settings.
The paper introduces the Machine Learning based Early Decision Making (ML-EDM) problem, which focuses on optimizing decision times in scenarios where data is collected over time, and identifies ten key challenges to guide future research in this area.
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.