With the rapid development of information technology, there are so much amount of data produced and collected in different domains. How to efficiently discover knowledge from these data has been a research focus. Classification algorithms, as a basic of data processing methods, are wildly used in data intelligent processing area. Traditional classification algorithms generally assume that the training sets are well-balanced with equal misclassification cost. However, data are usually class-imbalanced in real-world domains, which means one or some of the classes have less number of examples than others. Moreover, the minority class implies heavy cost when it is not well classified. The standard classification algorithms guided by global classification accuracy are often biased towards the majority class due to the imbalanced classification distribution. Therefore, it is required to enhance the accuracy of both the minority classes and the majority classes. The imbalanced data classification problem has received much attention from the machine learning community, and various approaches have been proposed to deal with the problem. This paper reviews the state-of-the-art imbalanced data classification methods in recent years, and analyzes and compares them comprehensively in accordance with essential difference from the data-preprocessing-level, feature-level and algorithm-level respectively. Then, considering the research focuses of machine learning field, the challenge of imbalanced data processing is discussed, followed with the prospects for future work.