2004 | OriginalPaper | Chapter
The Imbalanced Training Sample Problem: Under or over Sampling?
Authors : Ricardo Barandela, Rosa M. Valdovinos, J. Salvador Sánchez, Francesc J. Ferri
Published in: Structural, Syntactic, and Statistical Pattern Recognition
Publisher: Springer Berlin Heidelberg
Included in: Professional Book Archive
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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 domains, may produce an important deterioration of the classification accuracy, in particular with patterns belonging to the less represented classes. In this paper we present a study concerning the relative merits of several re-sizing techniques for handling the imbalance issue. We assess also the convenience of combining some of these techniques.