.png){kind=icon}
返回
A ranking-based problem transformation method for weakly supervised multi-label learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2024.110505.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Problem transformation is a simple yet effective framework for multi -label learning, where the original multilabel problem can be transformed into a series of single -label subproblems. However, the existing problem transformation methods have difficulties in handling label defect issues in real applications, e.g. multi -label learning with missing labels, partial multi -label learning and noisy multi -label learning. To deal with these issues, we propose a novel problem transformation method named EPR (i.e., Ensemble of Pairwise Ranking learners) applicable to various multi -label tasks. In EPR, the weakly supervised multi -label problem is converted into an ensemble of supervised single -label patterns due to pairwise label ranking, which successfully enhances label correlation exploration and improves the utilization of instances with defect labels. Moreover, an ensemble pruning mechanism is presented to heuristically balance the model performance and efficiency. Extensive experiments demonstrate the effectiveness of EPR against state-of-the-art algorithms in diverse multi -label learning scenarios.
Keyword:
Multi-label learning
Problem transformation
Pairwise label correlation
Ensemble learning
Multi-label learning with missing labels
Partial multi-label learning
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Learning Label-Specific Features and Class-Dependent Labels for Multi-Label Classification用于多标签分类的学习标签特定特征和类别相关标签
.png){kind=icon}
PREAI
分享收藏分享收藏
分享收藏
查看更多内容