Employing dropout regularization to classify recurring drifted data streams

Streaming data analysis is currently a rapidly growing research direction. One of the serious problems hindering the data stream classification is the fact that during the exploitation of the model, its probabilistic characteristics may change. This phenomenon is called concept drift. Until today, multiple methods have been proposed to overcome their negative influence on model performance during learning in dynamic environments. This work introduces a new streaming data classifier based on a dropout technique that can significantly reduce model restoration time and performance loss and can improve its overall score in the presence of recurring concept drifts. The usefulness of the proposed algorithm is evaluated based on extensive experimental study and backed-up with thorough statistical analysis.