Through the progress in each hardware and software system technologies, automatic data creation and storage have become quicker than ever. Such data is called as a data stream. Streaming information is present everywhere and it’s usually a difficult problem to visualize, collect and examine such huge volumes of information. Data stream mining has become a unique experimental area in information finding because of the large size and rapid speed of data in the data stream, due to this reason conventional classification methods are not effective. In today`s a substantial amount of analysis has been done on this issue whose main aim is to efficiently solve the difficulty of information stream mining with concept drift. Class imbalance is one of the problems of machine learning and data processing fields. Imbalance data sets reduce the performance as well as the overall accuracy of data mining methods. Decision making towards the majority class, which lead to misclassifying the minority class examples or moreover considered them as noise.