A Two-Dimensional Analysis of Resampling Techniques for Imbalanced Big Data Sets
DOI:
https://doi.org/10.30973/progmat/2019.11.1/6Keywords:
Big data, Inbalanced data, Over-sampling, under-sampligAbstract
Big data applications are the central focus of current research in many fields because of the increasing production of huge amounts of data. One important difficulty of data, both in traditional and big data real-life problems, refers to class imbalance where one class is heavily under-represented compared to the other classes. In this paper, we explore the use of some resampling methods for handling the class imbalance in big data sets, analysing processing time and classification accuracy achieved by three classifiers of different nature. The experimental results show that the over-sampling approaches perform better than the under-sampling techniques irrespective of the classifier used, but they lead to much higher classification times. When comparing the over-sampling methods, it is observed that SMOTE outperforms the random over-sampling algorithms; in the case of under-sampling, the random approach is better than the condensed nearest neighbour rule
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