Despite significant successes achieved in knowledge discovery,traditional machine leing methods may fail to obtain satisfactory performances when dealing with complex data,such as imbalanced,high-dimensional,noisy data,etc.The reason behind is that it is difficult for these methods to capture multiple characteristics and underlying structure of data.In this context,it becomes an important topic in the data mining field that how to effectively construct an efficient knowledge discovery and mining model.Ensemble leing,as one research hot spot,aims to integrate data fusion,data modeling,and data mining into a unified framework.Specifically,ensemble leing firstly extracts a set of features with a variety of transformations.Based on these leed features,multiple leing algorithms are utilized to produce weak predictive results.Finally,ensemble leing fuses the informative knowledge from the above results obtained to achieve knowledge discovery and better predictive performance via voting schemes in an adaptive way.In this paper,we review the research progress of the mainstream approaches of ensemble leing and classify them based on different characteristics.In addition,we present challenges and possible research directions for each mainstream approach of ensemble leing,and we also give an extra introduction for the combination of ensemble leing with other machine leing hot spots such as deep leing,reinforcement leing,etc.