http://www.cnr.it/ontology/cnr/individuo/prodotto/ID44154

Boosting multi-label hierarchical text categorization (Articolo in rivista)

Type

Prodotto della ricerca (Classe)
Articolo in rivista (Classe)

Label

Boosting multi-label hierarchical text categorization (Articolo in rivista) (literal)

Anno

2008-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

10.1007/s10791-008-9047-y (literal)

Alternative label

Esuli A.; Fagni T.; Sebastiani F. (2008)
Boosting multi-label hierarchical text categorization
in Information retrieval (Boston); Springer, Heidelberg (Germania)
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Esuli A.; Fagni T.; Sebastiani F. (literal)

Pagina inizio

287 (literal)

Pagina fine

313 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

http://www.springerlink.com/content/6r6567w636525024/?MUD=MP (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

11 (literal)

Rivista

Information retrieval (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note

In: Information Retrieval, vol. 11 (4) pp. 287 - 313. Springer Verlag, 2008. (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali

27 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

4 (literal)

Note

Google Scholar (literal)
Scopu (literal)
ISI Web of Science (WOS) (literal)
PuMa (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

ISTI-CNR, Pisa (literal)

Titolo

Boosting multi-label hierarchical text categorization (literal)

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. (literal)

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