Journal
INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE
Volume 29, Issue 4, Pages 769-781Publisher
UNIV ZIELONA GORA PRESS
DOI: 10.2478/amcs-2019-0057
Keywords
imbalanced data; multi-class learning; re-sampling; data difficulty factors; similarity degrees
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Funding
- Institute of Computing Science of the Poznan University of Technology
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The relations between multiple unbalanced classes can be handled with a specialized approach which evaluates types of examples' difficulty based on an analysis of the class distribution in the examples' neighborhood. additionally exploiting information about the similarity of neighboring classes. In this paper, we demonstrate that such an approach can be implemented as a data preprocessing technique and that it can improve the performance of various classifiers on multiclass Unbalanced datasets. It has led us to the introduction of a new resampling algorithm, called Similarity Oversampling and Undersampling Preprocessing (SOUP), which resamples examples according to their difficulty. Its experimental evaluation on real and artificial datasets has shown that it is competitive with the most popular decomposition ensembles and better than specialized preprocessing techniques for multi-imbalanced problems.
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