Imbalanced Data Stream Classification Using Hybrid Data Preprocessing

Abstract

Imbalanced data streams have gained significant popularity among the researchers in recent years. This area of research is not only still greatly underdeveloped, but there are also numerous inherent difficulties that need to be addressed when creating algorithms that could be utilized in such dynamic environment and achieve satisfactory results when it comes to their predictive abilities. In this paper, a novel algorithm that combines both over- and under-sampling techniques in order to create a more robust classifier dedicated to imbalanced data streams is proposed. The efficiency and high predictive quality of the proposed method have been confirmed on the basis of extensive experimental research carried out on the real and the computer-generated data streams.