1 research outputs found
A Mixtures-of-Experts Framework for Multi-Label Classification
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