Cluster-based under-sampling approaches for imbalanced data distributions

Authors:
Show-Jane Yen;Yue-Shi Lee
Affiliations:
Department of Computer Science and Information Engineering, Ming Chuan University, 5 The-Ming Road, Gwei Shan District, Taoyuan County 333, Taiwan;Department of Computer Science and Information Engineering, Ming Chuan University, 5 The-Ming Road, Gwei Shan District, Taoyuan County 333, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

For classification problem, the training data will significantly influence the classification accuracy. However, the data in real-world applications often are imbalanced class distribution, that is, most of the data are in majority class and little data are in minority class. In this case, if all the data are used to be the training data, the classifier tends to predict that most of the incoming data belongs to the majority class. Hence, it is important to select the suitable training data for classification in the imbalanced class distribution problem. In this paper, we propose cluster-based under-sampling approaches for selecting the representative data as training data to improve the classification accuracy for minority class and investigate the effect of under-sampling methods in the imbalanced class distribution environment. The experimental results show that our cluster-based under-sampling approaches outperform the other under-sampling techniques in the previous studies.