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 text classification datasets.
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Esuli, A., Sebastiani, F. (2009). Active Learning Strategies for Multi-Label Text Classification. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds) Advances in Information Retrieval. ECIR 2009. Lecture Notes in Computer Science, vol 5478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00958-7_12
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DOI: https://doi.org/10.1007/978-3-642-00958-7_12
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