A new over-sampling approach: Random-SMOTE for learning from imbalanced data sets

Abstract

For imbalanced data sets, examples of minority class are sparsely distributed in sample space compared with the overwhelming amount of majority class. This presents a great challenge for learning from the minority class. Enlightened by SMOTE, a new over-sampling method, Random-SMOTE, which generates examples randomly in the sample space of minority class is proposed. According to the experiments on real data sets, Random-SMOTE is more effective compared with other random sampling approaches. © 2011 Springer-Verlag.