ElStream: An Ensemble Learning Approach for Concept Drift Detection in Dynamic Social Big Data Stream Learning

With the rapid increase in communication technologies and smart devices, an enormous surge in data traffic has been observed. A huge amount of data gets generated every second by different applications, users, and devices. This rapid generation of data has created the need for solutions to analyze the change in data over time in unforeseen ways despite resource constraints. These unforeseeable changes in the underlying distribution of streaming data over time are identified as concept drifts. This paper presents a novel approach named
ElStream
that detects concept drift using ensemble and conventional machine learning techniques using both real and artificial data.
ElStream
utilizes the majority voting technique making only optimum classifier to vote for decision. Experiments were conducted to evaluate the performance of the proposed approach. According to experimental analysis, the ensemble learning approach provides a consistent performance for both artificial and real-world data sets. Experiments prove that the
ElStream
provides better accuracy of 12.49%, 11.98%, 10.06%, 1.2%, and 0.33% for PokerHand, LED, Random RBF, Electricity, and SEA dataset respectively, which is better as compared to previous state-of-the-art studies and conventional machine learning algorithms.