A One-per-Class reconstruction rule for class imbalance learning

Class imbalance limits the performance of most learning algorithms since they cannot cope with large differences between the number of samples in each class, resulting in a low predictive accuracy over the minority ones. Several algorithms achieving more balanced performance in case of binary learning have been proposed, while few researches exists in case of multi-class learning. This paper proposes a new reconstruction rule for the One-per-Class (OpC) decomposition method that, distinguishing between safe and dangerous classifications using sample classification reliability, compensates class imbalance in multiclass recognition problems and reduces effects due to the skewness between classes. The approach has been successfully tested on five datasets using three different classification architectures, and it favourably compares with results provided both by a multiclass classifier and by a popular OpC reconstruction rule.