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.
CITATION STYLE
Gulowaty, B., & Ksieniewicz, P. (2019). SMOTE Algorithm Variations in Balancing Data Streams. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11872 LNCS, pp. 305–312). Springer. https://doi.org/10.1007/978-3-030-33617-2_31
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