PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Esuli A., Sebastiani F. Active learning strategies for multi-label text classification. Technical report, 2008.
 
 
Abstract
(English)
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 text classification datasets.
Subject Active learning
Text classification
H.3 Information Storage and Retrieval
I.2.6 Learning
I.5.2 Design Methodology. Classifier design and evaluation


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