.png){kind=icon}
返回
Multi-label learning based on instance correlation and feature redundancy
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2023.10.029.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multi-label classification is a machine-learning task that simultaneously processes instances associated with multiple labels. Label-specific feature learning selects each label's most discriminative feature subset, effectively reducing the feature dimension and improving the classification performance. However, most methods only consider label correlation, ignoring the correlation between instances and feature redundancy. To solve this problem, a multi-label classification method based on instance correlation and feature redundancy is proposed. The proposed method merges instance correlation by updating the data set and removes redundant features by calculating mutual information. By jointly considering label correlation, instance correlation, and feature redundancy, our method promotes effective multi-label feature selection. The experimental results on ten data sets demonstrate the effectiveness of the proposed method.
Keyword:
Multi-label classification
Label-specific features
Instance correlation
Feature selection
期刊
IF:
3.3
论文数:
8.0K
被引数:
1.6W
机构
引用论文
Learning multi-label label-specific features via global and local label correlations通过全局和局部标签相关性学习多标签标签特定特征.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications关于处理数据稀缺的深度学习工具的调查: 定义，挑战，解决方案，技巧和应用JOURNAL OF BIG DATA
OAAI
JOURNAL OF BIG DATAIF6.4
OAAI分享收藏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
PREAI
分享收藏Deep-LIFT: Deep Label-Specific Feature Learning for Image AnnotationDeep-LIFT: 用于图像标注的深度标签特定特征学习
PREAI
分享收藏
查看更多内容