2008
DOI: 10.1007/s10791-008-9047-y
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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 algorit… Show more

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Cited by 61 publications
(39 citation statements)
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“…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.…”
Section: Preprocessing and Methods Usedmentioning
confidence: 99%
“…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.…”
Section: Preprocessing and Methods Usedmentioning
confidence: 99%
“…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.…”
Section: Accepted Manuscriptmentioning
confidence: 99%
“…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.…”
Section: The Metadata Classification Componentmentioning
confidence: 99%