期刊名称：International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
印刷版ISSN：2278-1323
出版年度：2017
卷号：6
期号：10
页码：1536-1540
出版社：Shri Pannalal Research Institute of Technolgy
摘要：Ensemble learning is used for data stream classification, as it facing problem to large size of stream data and concept drifting. Direct output of an extensive number of base classifiers in the troupe amid expectation keeping group gaining from being viable for some true time critical data stream applications, e.g. Web traffic. In this data streams usually come at a speed of GBPS, and it is important to order every stream record in a timely manner. That’s why we propose a novel E-tree indexing structure to sort out all bases in an ensemble for fast prediction. Heavy transaction loads at any servers eventually causes the break of the same. so many different steps are been taken to avoid this congestion of data streams. Most of the data stream techniques are based on the size of the data, this techniques are handling streams by maintaining a priority queue. These Methods of handling Data streams are bit old and they create chaos in the network instead of smooth handling. So as a solution for this Ensemble Learning gives a proper perception about data streaming using E-tree and R-tree Models, which successfully incorporates the streaming technique based on the past learning history.
关键词：Ensemble learning; Linear Clustering; Entropy Evaluation; E-Tree Formation
