Journal
NEUROCOMPUTING
Volume 149, Issue -, Pages 811-819Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2014.07.053
Keywords
Supervised topic model; Multi-label classification; Label frequency; Label dependency
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Funding
- National Nature Science Foundation of China (NSFC) [61170092, 61133011, 61103091]
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Recently, some publications indicated that the generative modeling approaches, i.e., topic models, achieved appreciated performance on multi-label classification, especially for skewed data sets. In this paper, we develop two supervised topic models for multi-label classification problems. The two models, i.e., Frequency-LDA (FLDA) and Dependency-Frequency-LDA (DFLDA), extend Latent Dirichlet Allocation (LDA) via two observations, i.e., the frequencies of the labels and the dependencies among different labels. We train the models by the Gibbs sampler algorithm. The experiment results on well known collections demonstrate that our two models outperform the state-of-the-art approaches. (C) 2014 Elsevier B.V. All rights reserved.
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