http://www.cnr.it/ontology/cnr/individuo/prodotto/ID44360

Selecting negative examples for hierarchical text classification: an experimental comparison (Articolo in rivista)

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Selecting negative examples for hierarchical text classification: an experimental comparison (Articolo in rivista) (literal)

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Fagni T.; Sebastiani F. (2010)
Selecting negative examples for hierarchical text classification: an experimental comparison
in Journal of the American Society for Information Science and Technology (Print); John Wiley & Sons, Ltd., New York (Stati Uniti d'America)
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http://onlinelibrary.wiley.com/doi/10.1002/asi.21411/abstract;jsessionid=38CE79E24E70B450B9FED865CFD02205.d01t04 (literal)

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In: Journal of the American Society for Information Science and Technology, vol. 61 (11) pp. 2256 - 2265. John Wiley & Sons, 2010. (literal)

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Titolo

Selecting negative examples for hierarchical text classification: an experimental comparison (literal)

Abstract

Hierarchical text classification (HTC) approaches have recently attracted a lot of interest on the part of researchers in human language technology and machine learning, since they have been shown to bring about equal, if not better, classification accuracy with respect to their \"flat\" counterparts while allowing exponential time savings at both learning and classification time. A typical component of HTC methods is a \"local\" policy for selecting negative examples: given a category c, its negative training examples are by default identified with the training examples that are negative for c and positive for the categories sibling to c in the hierarchy. However, this policy has always been taken for granted and never been subjected to careful scrutiny since first being proposed fifteen years ago. This paper proposes a thorough experimental comparison between this policy and three other policies for the selection of negative examples in HTC contexts, one of which (BestLocal(k)) is being proposed for the first time in this paper. We compare these policies on the hierarchical versions of three supervised learning algorithms (boosting, support vector machines, and naïve Bayes) by performing experiments on two standard TC datasets, Reuters-21578 and RCV1-v2. (literal)

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