Decomposition methods and learning approaches for imbalanced dataset: an experimental integration

Decomposition methods are multiclass classification schemes where the polychotomy is reduced into several dichotomies. Each dichotomy is addressed by a classifier trained on a training set derived from the original one on the basis of the decomposition rule adopted. These new training sets may present a disproportion between the classes, harming the global recognition accuracy. Indeed, traditional learning algorithms are biased towards the majority class, resulting in poor predictive accuracy over the minority one. This paper investigates if the application of learning methods specifically tailored for imbalanced training set introduces any performance improvement when used by dichotomizers of decomposition methods. The results on five public datasets show that the application of these learning methods improves the global performance of decomposition schemes.