A Comparative Analysis on Ensemble Classifiers for Concept Drifting Data Streams

Abstract

Mining in data stream plays a vital role in Big Data analytics. Traffic management, sensor networks and monitoring, weblogs analysis are the application of dynamic environments which generate streaming data. In a dynamic environment, data arrives at high speed and algorithms that process them need to fulfill the constraints on limited memory, computation time, and one-time scan of incoming data. The significant challenge in data stream mining is data distribution changes over a time period which is called concept drifts. So, learning model need to detect the changes and adapt according to that model. By nature, ensemble classifiers are adapting to changes very well and deal the concept drift very well. Three ensemble-based approaches were used to handle the concept drift: online, block-based ensemble, and hybrid approaches. We provide a survey on various ensemble classifiers for learning in data stream mining. Finally, we compare their performance on accuracy, memory, and time on synthetic and real datasets with different drift scenarios.