.png){kind=icon}
.png){kind=icon}
Return
Multi-label sampling based on local label imbalance
.png){kind=icon}
OA
AI
DOI：10.1016/j.patcog.2021.108294.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Class imbalance is an inherent characteristic of multi-label data that hinders most multi-label learning methods. One efficient and flexible strategy to deal with this problem is to employ sampling techniques before training a multi-label learning model. Although existing multi-label sampling approaches alleviate the global imbalance of multi-label datasets, it is actually the imbalance level within the local neighbour-hood of minority class examples that plays a key role in performance degradation. To address this issue, we propose a novel measure to assess the local label imbalance of multi-label datasets, as well as two multi-label sampling approaches, namely Multi-Label Synthetic Oversampling based on Local label imbal-ance (MLSOL) and Multi-Label Undersampling based on Local label imbalance (MLUL). By considering all informative labels, MLSOL creates more diverse and better labeled synthetic instances for difficult exam-ples, while MLUL eliminates instances that are harmful to their local region. Experimental results on 13 multi-label datasets demonstrate the effectiveness of the proposed measure and sampling approaches for a variety of evaluation metrics, particularly in the case of an ensemble of classifiers trained on repeated samples of the original data. (c) 2021 Elsevier Ltd. All rights reserved.
Keywords:
Multi-label learning
Class imbalance
Oversampling and undersampling
Local label imbalance
Ensemble methods
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W