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Paper details
Number 4 - December 2019
Volume 29 - 2019
Using information on class interrelations to improve classification of multiclass imbalanced data: A new resampling algorithm
Małgorzata Janicka, Mateusz Lango, Jerzy Stefanowski
Abstract
The relations between multiple imbalanced 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
imbalanced 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.
Keywords
imbalanced data, multi-class learning, re-sampling, data difficulty factors, similarity degrees