AAAI Publications, Twenty-Eighth AAAI Conference on Artificial Intelligence

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Multilabel Classification with Label Correlations and Missing Labels
Wei Bi, James T Kwok

Last modified: 2014-06-21

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.

Keywords


multi-label classification;missing labels

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