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A Graph-based Semi-supervised Multi-label Learning Method Based on Label Correlation Consistency

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Abstract

Multi-label learning deals with the problem which each data example can be represented by an instance and associated with a set of labels, i.e., every example can be classified into multiple classes simultaneously. Most of the existing multi-label learning methods are supervised which cannot deal with such application scenarios where manually labeling the data is very expensive and time-consuming while the unlabeled data are very cheap and easy to obtain. This paper proposes an ensemble learning method which integrates multi-label learning and graph-based semi-supervised learning into one framework. The label correlation consistency is introduced to deal with the multi-label learning. The proposed method has been evaluated on five public multi-label datasets by comparing it with state-of-the-art supervised and semi-supervised multi-label methods according to multiple evaluation metrics to confirm its effectiveness. Experimental results show that the proposed method can achieve the comparable performance compared with the state-of-the-art methods. Furthermore, it is more confident on every single predicted label.

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  1. http://mulan.sourceforge.net/datasets-mlc.html

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Acknowledgements

This work was supported by the Major Project for New Generation of AI under Grant No. 2018AAA0100400, the Open Project Funding of Shandong Key Laboratory of computer networks, the Qingdao Agricultural University Research Foundation for Advanced Talents under Grant No. 1119012, the Joint Fund of the Equipments Pre-Research and Ministry of Education of China under Grant No. 6141A020337, the Science and Technology Program of Qingdao under Grant No. 21-1-4-ny-19-nsh, and the Natural Science Foundation of Shandong Province under Grant No. ZR2020MF131.

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Authors and Affiliations

  1. College of Science and Information, Qingdao Agricultural University, Qingdao, 266109, China

    Qin Zhang

  2. Shandong Key Laboratory of Computer Networks, Jinan, 250014, China

    Qin Zhang

  3. Department of Computer Science and Technology, Ocean University of China, Qingdao, 266100, China

    Guoqiang Zhong & Junyu Dong

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  1. Qin Zhang
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  2. Guoqiang Zhong
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Correspondence to Guoqiang Zhong.

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Zhang, Q., Zhong, G. & Dong, J. A Graph-based Semi-supervised Multi-label Learning Method Based on Label Correlation Consistency. Cogn Comput 13, 1564–1573 (2021). https://doi.org/10.1007/s12559-021-09912-y

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