Many situations require decisions to be made quickly to avoid the costs associated with delaying the decision. A doctor who needs to choose which test to perform on their patient and an agent considering whether a certain behavior on a network is caused by a hacker are examples of individuals confronted with such situations. However, taking a decision too hastily may lead to more mistakes, resulting in additional costs that could have been avoided. In these situations, where there is a trade-off between the earliness of the decision and the accuracy of the prediction, the Machine Learning for Early Decision Making (ML-EDM) framework offers ML solutions not only to make a prediction but also to decide when to trigger its associated decision timely.
The ml_edm package provides tools to facilitate dealing with the Early Classification of Time Series (ECTS) problem, whose goal is to determine the class associated with a time series before all measurements are available.
pip install git+https://github.com/ML-EDM/ml_edmFor more information about the ML-EDM research domain, including latest publications, please have a look at the ML-EDM research GitHub: https://github.com/ML-EDM/ML-EDM.
If you use the package, please refer to it using the following the bibtex entry:
@misc{renault2024mledmpackagepythontoolkit,
title={ml_edm package: a Python toolkit for Machine Learning based Early Decision Making},
author={Aurélien Renault and Youssef Achenchabe and Édouard Bertrand and Alexis Bondu and Antoine Cornuéjols and Vincent Lemaire and Asma Dachraoui},
year={2024},
eprint={2408.12925},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2408.12925},
}