Deep reinforcement learning for multi-class imbalanced training: applications in healthcare.
Mach Learn
; 113(5): 2655-2674, 2024.
Article
de En
| MEDLINE
| ID: mdl-38708086
ABSTRACT
With the rapid growth of memory and computing power, datasets are becoming increasingly complex and imbalanced. This is especially severe in the context of clinical data, where there may be one rare event for many cases in the majority class. We introduce an imbalanced classification framework, based on reinforcement learning, for training extremely imbalanced data sets, and extend it for use in multi-class settings. We combine dueling and double deep Q-learning architectures, and formulate a custom reward function and episode-training procedure, specifically with the capability of handling multi-class imbalanced training. Using real-world clinical case studies, we demonstrate that our proposed framework outperforms current state-of-the-art imbalanced learning methods, achieving more fair and balanced classification, while also significantly improving the prediction of minority classes. Supplementary Information The online version contains supplementary material available at 10.1007/s10994-023-06481-z.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Langue:
En
Journal:
Mach Learn
Année:
2024
Type de document:
Article
Pays d'affiliation:
Royaume-Uni
Pays de publication:
États-Unis d'Amérique