Adaptive Weighted Learning for Unbalanced Multicategory Classification
Article first published online: 24 MAR 2008
© 2008, The International Biometric Society
Volume 65, Issue 1, pages 159–168, March 2009
How to Cite
Qiao, X. and Liu, Y. (2009), Adaptive Weighted Learning for Unbalanced Multicategory Classification. Biometrics, 65: 159–168. doi: 10.1111/j.1541-0420.2008.01017.x
- Issue published online: 17 MAR 2009
- Article first published online: 24 MAR 2008
- Received July 2007. Revised January 2008. Accepted January 2008.
- Adaptive learning;
- Mean within group error;
- Multicategory classification;
- Unbalanced data;
- Weighted learning
Summary In multicategory classification, standard techniques typically treat all classes equally. This treatment can be problematic when the dataset is unbalanced in the sense that certain classes have very small class proportions compared to others. The minority classes may be ignored or discounted during the classification process due to their small proportions. This can be a serious problem if those minority classes are important. In this article, we study the problem of unbalanced classification and propose new criteria to measure classification accuracy. Moreover, we propose three different weighted learning procedures, two one-step weighted procedures, as well as one adaptive weighted procedure. We demonstrate the advantages of the new procedures, using multicategory support vector machines, through simulated and real datasets. Our results indicate that the proposed methodology can handle unbalanced classification problems effectively.