SMOTE Algorithm Variations in Balancing Data Streams

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11872)


From one year to another, more and more vast amounts of data is being created in different fields of application. Great deal of those sources require real-time processing and analyzing, which leads to increased interest in streaming data classification field of machine learning. It is not rare, that many of those applications deal with somehow skewed or imbalanced data. In this paper, we analyze usage of smote oversampling algorithm variations in learning patterns from imbalanced data streams using different incremental learning ensemble algorithms.


Data streams Imbalanced learning Synthetic oversampling Classifier ensembles 



This work is supported by the Polish National Science Center under the Grant no. UMO-2015/19/B/ST6/01597 as well the statutory funds of the Department of Systems and Computer Networks, Faculty of Electronics, Wrocław University of Science and Technology.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Systems and Computer NetworksWroclaw University of Science and TechnologyWroclawPoland

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