Boosting multi-label hierarchical text categorization

Abstract

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.

Automated Classification and Categorization of Mathematical Knowledge

“…The other four were discarded on the ground of poor performance in preliminary experiments not reported here. On the other hand, there are several recent hierarchical classification algorithms [20] that we did not have time to explore yet.…”

An evaluation study on text categorization using automatically generated labeled dataset

“…Esuli et al [13] improved Adaboost for hierarchical categorization by feature selection and negative examples. They found that their TreeBoost could outperform Adaboost wrt both effective and efficiency.…”

Metadata Enrichment Services for the Europeana Digital Library

“…As the supervised learning technology we have used the TreeBoost system, a member of the family of "boosting"-based supervised learning algorithms that has shown state-of the-art accuracy across a variety of datasets [2]. TreeBoost allows the use of classification schemes organized either as a tree or as a directed acyclic graph.…”