An Efficient Model Ensemble Logical Bayesian Decision Trees for Ensemble Models On Data Streams

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Provided by: The International Journal of Innovative Research in Computer and Communication Engineering
Topic: Big Data
Format: PDF
Many real world applications, such as Web traffic stream monitoring, spam detection and intrusion detection and web click stream, generate continuously arriving data, known as data streams. To aid Ensemble Learning (EL) based decision making, to correctly classify an incoming data stream based on the model learnt from past labeled data. Existing studies, to date, have been mainly focused on building particular ensemble models from stream data. However, an Ensemble-tree (E-tree) indexing structure focusing the prediction incurs spatial or temporal data analysis in response time problem for where data nodes have arbitrary extents, which is a legitimate research problem well motivated by increasing real-time applications.
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