Abstract: Using ensemble of classifiers on sequential blocks of training instances is a popular strategy for data stream mining with concept drifts.Yet for the seriously unbalanced applications where the number of examples for each class in the data blocks is totally different,traditional data block creation will result in low accuracy for the small classes with much less number of instances.This paper provided an updating algorithm UMAE (Unbalanced data learning based on MAE) for seriously unbalanced applications based on MAE (Memorizing based Adaptive Ensemble).UMAE sets an equal-sized sliding window for each class.When each data block comes,each example in the data block comes into the corresponding sliding window based on its classes.During the learning process,a new data block will be created by using the instances in the current sliding windows.This new data block is adopted to generate a new classifier.Compared with five traditional data stream mining approaches,the results show that UMAE achieves high accuracy for seriously unba-lanced applications,especially for the small classes with much less number of instances in the applications.

Key words: Online learning,Data stream mining,Recalling and forgetting mechanisms,Unbalanced data learning