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Asymmetry label correlation for multi-label learning

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Abstract

As an effective method for mining latent information between labels, label correlation is widely adopted by many scholars to model multi-label learning algorithms. Most existing multi-label algorithms usually ignore that the correlation between labels may be asymmetric while asymmetry correlation commonly exists in the real-world scenario. To tackle this problem, a multi-label learning algorithm with asymmetry label correlation (ACML, Asymmetry Label Correlation for Multi-Label Learning) is proposed in this paper. First, measure the adjacency between labels to construct the label adjacency matrix. Then, cosine similarity is utilized to construct the label correlation matrix. Finally, we constrain the label correlation matrix with the label adjacency matrix. Thus, asymmetry label correlation is modeled for multi-label learning. Experiments on multiple multi-label benchmark datasets show that the ACML algorithm has certain advantages over other comparison algorithms. The results of statistical hypothesis testing further illustrate the effectiveness of the proposed algorithm.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61702012 and Key Laboratory of Data Science and Intelligence Application, Fujian Province University (NO. D202005) and Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education (Anhui University) (No.2020A003).

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Authors and Affiliations

  1. School of Computer and Information, Anqing Normal University, Anqing, 246011, Anhui, China

    Jiachao Bao, Yibin Wang & Yusheng Cheng

  2. School of Computer and Software Engineering, Anhui Institute of Information Technology, Wuhu, 241000, Anhui, China

    Jiachao Bao

  3. Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Zhangzhou, 363000, Fujian, China

    Yibin Wang & Yusheng Cheng

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  1. Jiachao Bao
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  2. Yibin Wang
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  3. Yusheng Cheng
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Correspondence to Yusheng Cheng.

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Bao, J., Wang, Y. & Cheng, Y. Asymmetry label correlation for multi-label learning. Appl Intell 52, 6093–6105 (2022). https://doi.org/10.1007/s10489-021-02725-4

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