incurrent era of big data, thedata analytics has become more challenging issue. Data get mined for finding facts as well as to predict impact of various activities which is used everywhere in the life. Mining processes like classification and clustering becomes more crucial in case of dynamic data streams. As the nature of data stream is temporal there is always a difference in the concepts which causes a concept drift. This concept drift affects the reliability of classifiers and clustering methods. Classification is important technique in data mining, which has been applied with various modifications to handle concept drift issue. Data stream classification is different from normal classification process as it has restriction of time, memory size and speed of processing along with accuracy. This article presents a review of remarkable recent ensemble based classifiers, which are designed to detect concept drift based on the different way of data stream processing.

Ensemble Learning, Data Stream Mining, Concept Drift

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