Active Learning Strategies for Multi-Label Text Classification

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Active Learning Strategies for Multi-Label Text Classification
Abstract. Active learning refers to the task of devising a ranking function that, given a classifier trained from relatively few training examples, ranks a set of additional unlabeled examples in terms of how much further information they would carry, once manually labeled, for retraining a (hopefully) better classifier. Research on active learning in text classification has so far concentrated on single-label classification; active learning for multi-label classification, instead, has either been tackled in a simulated (and, we contend, non-realistic) way, or neglected tout court. In this paper we aim to fill this gap by examining a number of realistic strategies for tackling active learning for multi-label classification. Each such strategy consists of a rule for combining the outputs returned by the individual binary classifiers as a result of classifying a given unlabeled document. We present the results of extensive experiments in which we test these strategies on two standard tex...
Andrea Esuli, Fabrizio Sebastiani
Added 17 Feb 2011
Updated 17 Feb 2011
Type Journal
Year 2009
Where ECIR
Authors Andrea Esuli, Fabrizio Sebastiani
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