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Kernel-Based Learning of Hierarchical Multilabel Classification Models

Rousu, J., Saunders, C., Szedmak, S. and Shawe-Taylor, J. (2006) Kernel-Based Learning of Hierarchical Multilabel Classification Models Journal of Machine Learning Research, 7, pp. 1601-1626.

Record type: Article


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. Training of the full hierarchical model is as efficient as training independent SVM-light 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.

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Published date: July 2006
Keywords: kernel methods, text classification
Organisations: Electronics & Computer Science


Local EPrints ID: 262947
PURE UUID: 6c0445c5-ffec-4fdd-b509-c50ec6116cdd

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Date deposited: 07 Sep 2006
Last modified: 18 Jul 2017 08:46

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Author: J. Rousu
Author: C. Saunders
Author: S. Szedmak
Author: J. Shawe-Taylor

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