Impact of Randomization on Ensembles for Streams with Concept Drift

In the present era many real world applications are built from streaming data. The distribution of underlying data in such streams tend to change with course of time called as concept drift. A lot of algorithms are proposed in machine learning and data mining domain which are used to handle this drifting scenario. Ensembles form an integral component of such algorithms. In this paper, randomization is added using online bagging to the existing four drift handling approaches and its effect is analysed over multiple patterns of concept drift such as gradual, abrupt, recurring etc. Experimental work has been conducted over artificially generated data streams and real datasets to validate the impact of bagging in learning process.