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arXiv · 1409.4698

A Mixtures-of-Experts Framework for Multi-Label Classification

Charmgil Hong·Iyad Batal·Milos Hauskrecht
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Abstract

We develop a novel probabilistic approach for multi-label classification that is based on the mixtures-of-experts architecture combined with recently introduced conditional tree-structured Bayesian networks. Our approach captures different input-output relations from multi-label data using the efficient tree-structured classifiers, while the mixtures-of-experts architecture aims to compensate for the tree-structured restrictions and build a more accurate model. We develop and present algorithms for learning the model from data and for performing multi-label predictions on future 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.

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Charmgil Hong, Iyad Batal, Milos Hauskrecht. 2014-09-16. A Mixtures-of-Experts Framework for Multi-Label Classification. https://arxiv.org/abs/1409.4698

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