A Mixtures-of-Experts Framework for Multi-Label Classification
This work addresses multi-label classification, a common problem in machine learning for tasks like text categorization, but it appears incremental as it combines existing architectures.
The paper tackles multi-label classification by proposing a probabilistic mixtures-of-experts framework with conditional tree-structured Bayesian networks, achieving competitive results and outperforming state-of-the-art methods on benchmark datasets.
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.