Abstract
In many real world machine learning tasks, labeled training examples are expensive to obtain, while at the same time there is a lot of unlabeled examples available. One such class of learning problems is text classification. Active learning strives to reduce the required labeling effort while retaining the accuracy by intelligently selecting the examples to be labeled. However, very little comparison exists between different active learning methods. The effects of the ratio of positive to negative examples on the accuracy of such algorithms also received very little attention. This paper presents a comparison of two most promising methods and their performance on a range of categories from the Reuters Corpus Vol. 1 news article dataset.
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© 2006 Springer Berlin · Heidelberg
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Novak, B., Mladenič, D., Grobelnik, M. (2006). Text Classification with Active Learning. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_48
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DOI: https://doi.org/10.1007/3-540-31314-1_48
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