The Impact of Local Data Characteristics on Learning from Imbalanced Data

Problems of learning classifiers from imbalanced data are discussed. First, we look at different data difficulty factors corresponding to complex distributions of the minority class and show that they could be approximated by analysing the neighbourhood of the learning examples from the minority class. We claim that the results of this analysis could be a basis for developing new algorithms. In this paper we show such possibilities by discussing modifications of informed pre-processing method LN-SMOTE as well as by incorporating types of examples into rule induction algorithm BRACID.