Review paper on adapting data stream mining concept drift using ensemble classifier approach
Nilima Motghare, Arvind Mewada · IOSR Journal of Computer Engineering · 2014
Data stream is massive, fast changing and infinite in nature.It is very natural that large amount of unlabeled data and small amount labeled are available in data stream environments.Storing and labeling all data is considered expensive and impractical.The objective is to label small portion of stream data and analyze data online without storing it.Concept drift, concept evolving, stream evolving is also the major challenging problem occurs while working with data stream.Online data stream active learning is needed to tackle these problems.Classification and clustering are two technical areas that are widely used to extract pattern from the large data stream, from that a classification model must endlessly adapt itself to the most recent concept.Hence, this paper gives the overview of various ensemble based classification algorithm techniques in the field of data stream mining.