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Journal of Classification

22-08-2024

Clustering-Based Oversampling Algorithm for Multi-class Imbalance Learning

Authors: Haixia Zhao, Jian Wu

Published in: Journal of Classification

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Abstract

Multi-class imbalanced data learning faces many challenges. Its complex structural characteristics cause severe intra-class imbalance or overgeneralization in most solution strategies. This negatively affects data learning. This paper proposes a clustering-based oversampling algorithm (COM) to handle multi-class imbalance learning. In order to avoid the loss of important information, COM clusters the minority class based on the structural characteristics of the instances, among which rare instances and outliers are carefully portrayed through assigning a sampling weight to each of the clusters. Clusters with high densities are given low weights, and then, oversampling is performed within clusters to avoid overgeneralization. COM avoids intra-class imbalance effectively because low-density clusters are more likely than high-density ones to be selected to synthesize instances. Our study used the UCI and KEEL imbalanced datasets to demonstrate the effectiveness and stability of the proposed method.

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Appendix
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Metadata
Title
Clustering-Based Oversampling Algorithm for Multi-class Imbalance Learning
Authors
Haixia Zhao
Jian Wu
Publication date
22-08-2024
Publisher
Springer US
Published in
Journal of Classification
Print ISSN: 0176-4268
Electronic ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-024-09491-1

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