Review on ensemble algorithms for imbalanced data classification.

Ensemble learning by integrating multiple base classifiers that trained different set can effectively improve the classification accuracy. In the base classifier training process, imbalanced data set can be processed by either cost-sensitive or data sampling technology. Due to the advantages of ensemble learning in imbalanced data classification, ensemble algorithms for imbalanced data classification have been widely research. This paper surveyed the state of the art of imbalanced data ensemble classification algorithms, including the mechanisms and features of major existing learning algorithms, their advantages and disadvantages, highlighted the open research issues and future research directions. [ABSTRACT FROM AUTHOR]