Group-Label-Specific Features Learning Based on Label-Density Classification Margin

Yibin WANG; Gensheng PEI; Yusheng CHENG

doi:10.11999/JEIT190343

Volume 42 Issue 5

Jun. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(5): 1179-1187

Yibin WANG, Gensheng PEI, Yusheng CHENG. Group-Label-Specific Features Learning Based on Label-Density Classification Margin[J]. Journal of Electronics & Information Technology, 2020, 42(5): 1179-1187. doi: 10.11999/JEIT190343

Citation:

Yibin WANG, Gensheng PEI, Yusheng CHENG. Group-Label-Specific Features Learning Based on Label-Density Classification Margin[J]. Journal of Electronics & Information Technology, 2020, 42(5): 1179-1187. doi: 10.11999/JEIT190343

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Group-Label-Specific Features Learning Based on Label-Density Classification Margin

doi: 10.11999/JEIT190343

Yibin WANG^{1, 2},
Gensheng PEI¹,
Yusheng CHENG^{1, 2
,
,}

1.
School of Computer and Information, Anqing Normal University, Anqing 246011, China
2.
The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing 246011, China

Funds: The Natural Science Foundation of Anhui Province

Received Date: 2019-05-18
Rev Recd Date: 2019-09-30

Available Online: 2020-01-29

Publish Date: 2020-06-04

Abstract

Abstract

The label-specific features learning avoids the same features prediction for all class labels, it is a kind of framework for extracting the specific features of each label for classification, so it is widely used in multi-label learning. For the problems of large label dimension and unbalanced label distribution density, the existing multi-label learning algorithm based on label-specific features has larger time consumption and lower classification accuracy. In order to improve the performance of classification, a Group-Label-Specific Features Learning method based on Label-Density Classification Margin (GLSFL-LDCM) is proposed. Firstly, the cosine similarity is used to construct the label correlation matrix, and the class labels are grouped by spectral clustering to extract the label-specific features of each label group to reduce the time consumption for calculating the label-specific features of all class labels. Then, the density of each label is calculated to update the label space matrix, the label-density information is added to the original label space. The classification margin between the positive and negative labels is expanded, thus the imbalance label distribution density problem is effectively solved by the method of label-density classification margin. Finally, the final classification model is obtained by inputting the group-label-specific features and the label-density matrix into the extreme learning machine. The comparison experiment results verify fully the feasibility and stability of the proposed algorithm.
- Multi-label classification,
- Label-density,
- Group-label-specific features,
- Extreme learning machine,
- Classification margin

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