earticle

영어

Data Stream is a continuous set of data records. When data arrive at a very high speed and continuously, so predicting the class in timely manner is important. Class prediction of data Stream is an important task in data mining. Nowadays Ensemble Modeling technique growing rapidly in Data Stream Classification. Ensemble learning become popular because of its advantage to handle large quantity of data stream, means it can handle the data in a bulk and also it can handle concept drifting. Earlier studies, mostly focused on accuracy of ensemble model, prediction efficiency has not considered much because existing ensemble model predicts in linear time, which is enough for general or small applications and existing models works on integrating small number of classifier. But real world application have large volume of data stream so we need more base classifier to identify different patterns and build a high grade ensemble model. To overcome these challenge we propose height balanced tree indexing structure (Ensemble tree) of base classifier for fast prediction on data streams by ensemble modeling technique. Ensemble Tree handles ensembles as spatial databases and it make use of an R-tree like structure to achieve sub linear time complexity.