Multilabel Classification with Label Correlations and Missing Labels

Abstract

Many real-world applications involve multilabel classification, in which the labels can have strong interdependencies 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… (More)

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