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.
翻译:越来越多的应用需要尽早作出决定,即尽快从部分观察的数据中作出决定,然而,越晚作出决定,其准确性就越有改善,因为随着时间的推移,对问题的说明会更加丰富,在早期时间序列分类领域特别研究了决定的耳度和准确性之间的这种妥协,本文件提出了一个更普遍的问题,称为基于机械学习的早期决策(ML-EDM),其中包括在长期收集数据的广泛环境中优化模型的决策时间,在界定ML-EDM问题之后,确定了10项挑战,并向科学界提出,以进一步研究这一领域,这些挑战开放了重要的应用观点,本文件对此进行了讨论。