ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (1): 34-47.doi: 10.7544/issn1000-1239.2021.20190674

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Label-Specific Features Learning for Feature-Specific Labels Association Mining

Cheng Yusheng1,2, Zhang Lulu1, Wang Yibin1,2, Pei Gensheng1   

  1. 1(School of Computer and Information, Anqing Normal University, Anqing, Anhui 246133);2(Key Laboratory of Intelligent Computing & Signal Processing (Anhui University), Ministry of Education, Hefei 230601)
  • Online:2021-01-01
  • Supported by: 
    This work was supported by the General Program of the National Natural Science Foundation of China(61702012) and the Fund of Key Laboratory of Intelligent Computing & Signal Processing(Anhui University), Ministry of Education (2020A003).

Abstract: In multi-label learning, a label may be determined by its own set of unique features only, which are called label-specific features. Using label-specific features in multi-label classification can effectively avoid some useless features affecting the performance of the constructed classification model. However, existing label-specific features methods only extract important features from the label’s perspective, while ignoring extracting important labels from the feature’s perspective. In fact, it’s easier to extract the unique features for labels by focusing on certain labels from the feature’s perspective. Based on this, a novel label-specific features learning algorithm for multi-label classification is proposed. It combines the label’s important features with the feature’s important labels. Firstly, in order to ensure the efficiency and accuracy of the model, the extreme learning machine is used to construct the joint learning model. Subsequently, the elastic network regularization theory is applied to the extreme learning machine’s loss function, and the mutual information theory is used to construct the correlation matrix of feature-specific labels as the L\-2 regularization term, and the label-specific features are extracted by the L\-1 regularization term. The learning model improves the deficiencies of label-specific features and the adaptability of the extreme learning machine in multi-label learning. Compared with several state-of-the-art algorithms on several benchmark multi-label datasets, the experimental results show the rationality and effectiveness of the proposed model.

Key words: multi-label learning, label-specific features, feature-specific labels, extreme learning machine, label correlation

CLC Number: