Racing for unbalanced methods selection

Abstract

State-of-the-art classification algorithms suffer when the data is skewed towards one class. This led to the development of a number of techniques to cope with unbalanced data. However, as confirmed by our experimental comparison, no technique appears to work consistently better in all conditions. We propose to use a racing method to select adaptively the most appropriate strategy for a given unbalanced task. The results show that racing is able to adapt the choice of the strategy to the specific nature of the unbalanced problem and to select rapidly the most appropriate strategy without compromising the accuracy. © 2013 Springer-Verlag.