Boosting multi-label hierarchical text categorization

Hierarchical Text Categorization (HTC) is the task of generating (usually by means of supervised learning algorithms) text classifiers that operate on hierarchically structured classification schemes. Notwithstanding the fact that most large-sized classification schemes for text have a hierarchical structure, so far the attention of text classification researchers has mostly focused on algorithms for flat classification, i.e. algorithms that operate on non-hierarchical classification schemes. These algorithms, once applied to a hierarchical classification problem, are not capable of taking advantage of the information inherent in the class hierarchy, and may thus be suboptimal, in terms of efficiency and/or effectiveness. In this paper we propose TREEBOOST.MH, a multi-label HTC algorithm consisting of a hierarchical variant of ADABOOST.MH, a very well-known member of the family of boosting learning algorithms. TREEBOOST.MH embodies several intuitions that had arisen before within HTC: e.g. the intuitions that both feature selection and the selection of negative training examples should be performed locally, i.e. by paying attention to the topology of the classification scheme. It also embodies the novel intuition that the weight distribution that boosting algorithms update at every boosting round should likewise be updated locally. All these intuitions are embodied within TREEBOOST.MH in an elegant and simple way, i.e. by defining TREEBOOST.MH as a recursive algorithm that uses ADABOOST.MH as its base step, and that recurs over the tree structure. We present the results of experimenting TREEBOOST.MH on three HTC benchmarks, and discuss analytically its computational cost.