Experimental Study on Modified Radial-Based Oversampling

  • Barbara BobowskaEmail author
  • Michał Woźniak
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 771)


Although, imbalanced data analysis gained significant attention in the past years, it still remains an underdeveloped area of research posing many difficulties due to the difference in the number of objects in the examined classes, rendering traditional, accuracy driven machine learning methods useless. With many modern real-life applications being examples of imbalanced data classification i.e. fraud detection, medical diagnosis, oil-spills detection in satellite images or network anomaly detection, it is crucial to develop new algorithms suitable to use in such situations. One of the approaches to deal with the disproportion between the instances of objects in classes are either over- or undersampling techniques. In this paper, we propose a modification of an existing RBO algorithm. Due to the additional constraint the modified algorithm eliminates instances which may be problematic to classify. Additionally, a recursion mechanism was added in order to make the search of synthetic points more robust. The results obtained from computer experiments carried out on the benchmark datasets prove that the presented algorithm is applicable.



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 International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Electronics, Department of Systems and Computer NetworksWrocław University of Science and TechnologyWrocławPoland

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