A Data Intensive Multi-chunk Ensemble Technique to Classify Stream Data Using Map-Reduce Framework

We propose a data intensive and distributed multichunk ensemble classifier based data mining technique to classify data streams. In our approach, we combine r most recent consecutive data chunks with data chunks in the current ensemble and generate a new ensemble using this data for training. By introducing this multi-chunk ensemble technique in a Map-Reduce framework and considering the concept-drift of the data, we significantly reduce the running time and classification error compared to different ensemble approaches. We have empirically proved its effectiveness over other state-of-the-art stream classification techniques on synthetic data and real world botnet traffic.