Multi-Label Learning With Label Specific Features Using Correlation Information

IEEE Access

Huirui Han¹  Xiaogang Yang¹  Wenlong Feng¹  Yu Zhang¹  Mengxing Huang¹ 

[1] State Key Laboratory of Marine Resource Utilization in South China, College of Information Science and Technology, Hainan University, Haikou, China;

关键词: Feature selection;    multi-label learning;    label specific features;    label correlation;   

DOI  :  10.1109/ACCESS.2019.2891611

来源: DOAJ

【 摘 要 】

To deal with the problem where each instance is associated with multiple labels, a lot of multi-label learning algorithms have been developed in recent years. Some approaches have been proposed to select label-specific features to utilize discriminate features for multi-label classification. Although label correlation has been considered in learning label-specific features, the critical correlation among instances was less taken into account. In this paper, we proposed a new approach called multi-label learning with label-specific features using correlation information (LSF-CI) to learn label-specific features for each label with the consideration of both correlation information in label space and correlation information in feature space. In the LSF-CI, the instance correlation in feature space is computed by a probabilistic neighborhood graph model, and label correlation in label space is computed by cosine similarity. For multi-label data, the LSF-CI has the capability to select Label-specific features for each label as well as classify an unseen instance into a set of relevant labels. To validate the effectiveness of LSF-CI, we conducted comprehensive experiments on eight multi-label datasets. The experimental results demonstrate that the LSF-CI is capable of selecting compact label-specific features, and achieving a competitive performance in comparison with the performances of the existing multi-label learning approaches.

【 授权许可】

Unknown