Abstract
A problem of supervised approaches for text classification is that they commonly require high-quality training data to construct an accurate classifier. Unfortunately, in many real-world applications the training sets are extremely small and present imbalanced class distributions. In order to confront these problems, this paper proposes a novel approach for text classification that combines under-sampling with a semi-supervised learning method. In particular, the proposed semi-supervised method is specially suited to work with very few training examples and considers the automatic extraction of untagged data from the Web. Experimental results on a subset of Reuters-21578 text collection indicate that the proposed approach can be a practical solution for dealing with the class-imbalance problem, since it allows achieving very good results using very small training sets.
This work was done under partial support of CONACYT-Mexico (43990) MCyT-Spain (TIN2006-15265-C06-04) and PROMEP (UGTO-121).
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Guzmán-Cabrera, R., Montes-y-Gómez, M., Rosso, P., Villaseñor-Pineda, L. (2007). Taking Advantage of the Web for Text Classification with Imbalanced Classes. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_79
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DOI: https://doi.org/10.1007/978-3-540-76631-5_79
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