Exploring Data Stream Classification: Comparative Review of Single and Ensemble Classifier Approaches

With the increasing use of technology stream classification is important dynamic and changing nature. Data in the data stream comes at a very high speed thus it has to be taken care of in real-time. Due to its complex nature, certain problems can arise over time. To handle these problems better, the requirement for competent and effectual techniques has increased. Regular static data classification techniques cannot be used for classification in Data steam. This paper aims to explore different Learning algorithms that were proposed for successful classification in streaming data over the years. The paper describes single classifier and ensemble classifiers algorithms. The paper shows the streaming data classification research along with its comparison. We also reviewed and compared several elements of the previously presented potential algorithms. We have outlined other crucial aspects that each algorithm addresses.