Adaptive weighted learning for unbalanced multicategory classification.
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.
Qiao
Xingye
X
Department of Statistics and Operations Research, Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, North Carolina 27599, USA.
Liu
Yufeng
Y
D001185:Artificial Intelligence; D001699:Biometry / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D008962:Models, Theoretical