Open challenges for Machine Learning based Early Decision-Making research

TL;DR

This paper introduces the broader ML-EDM problem, highlighting challenges in optimizing early decision times across various data collection scenarios, and discusses future research directions and applications.

Contribution

It generalizes early decision-making beyond time series classification, defining ML-EDM and proposing ten key research challenges for the community.

Findings

Identifies ten research challenges in ML-EDM

Highlights application opportunities in early decision-making

Defines a general framework for early decision problems

Abstract

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.