Kernel-based Learning of Hierarchical Multilabel Classification Models
Juho Rousu, Craig Saunders, Sandor Szedmak and John Shawe-Taylor
Journal of Machine Learning Research
We present a kernel-based algorithm for hierarchical text
classification where the documents are allowed to belong to more
than one category at a time. The classification model is a variant
of the Maximum Margin Markov Network framework, where the
classification hierarchy is represented as a Markov tree equipped
with an exponential family defined on the edges.
We present an efficient optimization
algorithm based on incremental conditional gradient ascent in
single-example subspaces spanned by the marginal dual variables.
The optimization is facilitated with a dynamic programming based
algorithm that computes best update directions in the feasible set.
Experiments show that the algorithm can
feasibly optimize training sets of thousands of examples and
classification hierarchies consisting of hundreds of nodes. In our tests,
training of the full hierarchical model was in fact faster than training inpedendent
SVM classifiers for each node.
The algorithm's predictive accuracy was found to be competitive with
other recently introduced hierarchical multi-category or
multilabel classification learning algorithms.