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- W65280498 abstract "Many real-world applications involve multilabel classification, in which the labels can have strong inter-dependencies and some of them may even be missing.Existing multilabel algorithms are unable to handle both issues simultaneously.In this paper, we propose a probabilistic model that can automatically learn and exploit multilabel correlations.By integrating out the missing information, it also provides a disciplinedapproach to the handling of missing labels. The inference procedure is simple, and the optimization subproblems are convex. Experiments on a number of real-world data sets with both complete and missing labelsdemonstrate that the proposed algorithm can consistently outperform state-of-the-art multilabel classification algorithms." @default.
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- W65280498 date "2014-06-21" @default.
- W65280498 modified "2023-10-05" @default.
- W65280498 title "Multilabel Classification with Label Correlations and Missing Labels" @default.
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- W65280498 doi "https://doi.org/10.1609/aaai.v28i1.8996" @default.
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