MLAWSMOTE: Oversampling in Imbalanced Multi-label Classification with Missing Labels by Learning Label Correlation Matrix

Abstract Missing labels in multi-label datasets are a common problem, especially for minority classes, which are more likely to occur. This limitation hinders the performance of classifiers in identifying and extracting information from minority classes. Oversampling is an effective method for addre...

Full description

Bibliographic Details

Published in:	International Journal of Computational Intelligence Systems
Main Authors:	Jian Mao, Kai Huang, Jinming Liu
Format:	Article
Language:	English
Published:	Springer 2024-08-01
Subjects:	Multi-label learning Imbalanced learning Missing labels Oversampling methods
Online Access:	https://doi.org/10.1007/s44196-024-00607-4