We develop a novel probabilistic ensemble framework for multi-label classification that is based on the mixtures-of-experts architecture. In this framework, we combine multi-label classification models in the classifier chains family that decompose the class posterior distribution P(Y1, Yd|X) using a product of posterior distributions over components of the output space. Our approach captures different input-output and output-output relations that tend to change across data. As a result, we can recover a rich set of dependency relations among inputs and outputs that a single multi-label classification model cannot capture due to its modeling simplifications. We develop and present algorithms for learning the mixtures-of-experts models from data and for performing multi-label predictions on unseen data instances. Experiments on multiple benchmark datasets demonstrate that our approach achieves highly competitive results and outperforms the existing state-of-the-art multi-label classification methods.
CITATION STYLE
Hong, C., Batal, I., & Hauskrecht, M. (2015). A generalized mixture framework for multi-label classification. In SIAM International Conference on Data Mining 2015, SDM 2015 (pp. 712–720). Society for Industrial and Applied Mathematics Publications. https://doi.org/10.1137/1.9781611974010.80
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