A self-organizing incremental neural network for imbalance learning

Class imbalance learning deals with data that have very skewed class distributions, and commonly exists in real-world applications. Incremental learning has the ability to train a model continually using new data, which requires the model to learn the new information without forgetting the old one. While these two issues have been independently discussed, their joint treatment has not been studied thoroughly. This paper studies the combined challenges and proposes a balanced self-organizing incremental neural network (Balanced SOINN). First, we introduce Balanced SOINN, which can be trained incrementally with the resampling method. Then, we compare Balanced SOINN with other methods with artificial and real-world data. Our proposed method is competitive in artificial datasets in non-incremental scenarios and achieves the best performance with real-world datasets in incremental scenarios.