Abstract
The problem of imbalanced training sets in supervised pattern recognition methods is receiving growing attention. Imbalanced training sample means that one class is represented by a large number of examples while the other is represented by only a few. It has been observed that this situation, which arises in several practical situations, may produce an important deterioration of the classification accuracy, in particular with patterns belonging to the less represented classes. In the present paper, we introduce a new approach to design an instance-based classifier in such imbalanced environments.
Partially supported by grants 32016-A (Mexican CONACyT), TIC2000-1703-C03-03 (Spanish CICYT), and P1-1B2002-07 (Fundació Caixa Castelló-Bancaixa).
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Barandela, R., Sánchez, J.S., García, V., Ferri, F.J. (2003). Learning from Imbalanced Sets through Resampling and Weighting. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_10
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DOI: https://doi.org/10.1007/978-3-540-44871-6_10
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