Proceedings of the 33rd International Conference on Machine
Learning (ICML), New York City, NY, 2016.
[Paper] [Supplementary
Material] [Slides]
[Poster] [Code] [BibTex]
Multi-label classification is an important machine learning task
wherein one assigns a subset of candidate labels to an object. In
this paper, we propose a new multi-label classification method
based on Conditional Bernoulli Mixtures. Our proposed method has
several attractive properties: it captures label dependencies; it
reduces the multi-label problem to several standard binary and
multi-class problems; it subsumes the classic independent binary
prediction and power-set subset prediction methods as special
cases; and it exhibits accuracy and/or computational complexity
advantages over existing approaches. We demonstrate two
implementations of our method using logistic regressions and
gradient boosted trees, together with a simple training procedure
based on Expectation Maximization. We further derive an efficient
prediction procedure based on dynamic programming, thus avoiding
the cost of examining an exponential number of potential label
subsets. Experimental results show the effectiveness of the
proposed method against competitive alternatives on benchmark
datasets.