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Self-dependence multi-label learning with double k for missing labels
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DOI：10.1007/s10462-022-10279-1.png)
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Abstract
En 中文
Multi-label learning with missing labels is a challenging task, especially in text classification applications. Existing approaches considering label correlations are effective in recovering missing labels. However, they are often unstable because severely imbalanced positive and negative labels are treated in the same way. In this paper, we propose the self-dependence multi-label learning with a double k label recovery algorithm to address this issue. First, two label count matrices are constructed from the original label matrix from the perspective of positive and negative labels independently. This is done through statistics on the k nearest neighbors according to the input features. Second, positive and negative label matrices are decomposed and recovered using matrix factorization, namely double k (k nearest neighbors and k latent semantics). Third, new features are generated according to the recovered matrices by label concept. Fourth, we exploit the independence of the labels to guide the training process. Extensive experiments and analyses on multiple benchmark data sets illustrate the effectiveness of the proposed method. In addition, with the increase of missing labels, the stability of our algorithm becomes significantly better than the state-of-the-art ones.
Keywords:
Multi-label learning
Double k
Missing labels
Label concept
Self-dependence
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