Multi-class imbalance: an evidential hybrid resampling approach

Learning from class-imbalanced datasets has gained substantial attention in machine learning community. Yet, there has been little emphasis given to dealing with multiclass imbalance learning. In this paper, we present an evidential hybrid resampling method for dealing with class imbalance in the multi-class setting. This technique uses the belief function theory to assign a soft label to each object. This evidential modeling provides more information about each object’s region, which improves the selection of objects in both undersampling and oversampling. An adjustment has also been integrated in order to avoid excessive oversampling and undersampling. Benchmarking results have shown significant improvement of G-Mean of AUC metrics over other popular resampling methods.