Adequacy of Effectual Ensemble Classification Approach to Detect Drift in Data Streams

Data stream mining has become an essential task in the current scenario as most of the applications are producing a wide range of information in the form of streams. It could be in education, IoT, social media, entertainment, banking, and a variety of other fields. This stream of data has several unique properties, such as volume, creation speed, and the inventiveness of concepts at any given time. This vividness of data is the most difficult aspect of data processing, and thus data mining and analysis. In this study, we examine the major three strategies for constructing ensemble-based classifiers with datasets with varying drifts in depth. This research has yielded data that will provide further insight into the building of ensembles for various environments.