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As an important part of machine learning, classification learning has been applied in many practical fields. It is valuable that to discuss class imbalance learning in several fields. In this research, we provide a review of class imbalanced learning methods from the data driven methods and algorithm driven methods based on numerous published papers which studied class imbalance learning. The preliminary analysis shows that class imbalanced learning methods mainly are applied both management and engineering fields. Firstly, we analyze and then summarize resampling methods that are used in different stages. Secondly, we provide a detailed instruction on different algorithms, and then we compare the results of decision tree classifiers based on resampling and empirical cost sensitivity. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the class imbalanced learning fields.