Affiliations: [a] School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China | [b] Department of Radiology, Carver College of Medicine, The University of Iowa, Iowa, IA, USA | [c] School of Information Engineering, Yancheng Institute of Technology, Yancheng, Jiangsu, China
Correspondence:
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Corresponding author: Hualong Yu, School of Computer Science and Engineering, Jiangsu University of Science and Technology, No.2, Mengxi Road, Zhenjiang 212003, Jiangsu, China. Tel.: +86 511 88690470; Fax: +86 511 88690471; E-mail: [email protected].
Abstract: In many real world applications, class imbalance problems occur frequently, causing great underestimation for the classification performance of minority classes. In recent years, much effective solutions have been proposed to address this problem. However, the recent research found that not all skewed classification tasks are harmful and carrying out class imbalance learning methods on those unharmful ones can hardly improve and even degenerate classification performance, meanwhile increase training time to a large extent. Therefore, it is essential to design an efficient criterion to pre-estimate the harmfulness of class imbalance when we encounter skewed classification tasks. In this study, we explore the reason of harmfulness produced by class imbalance and propose a simple and ingenious strategy using scatter matrix based class separability measure to estimate the harmfulness of class imbalance with merely using training samples. The experimental results indicate that the proposed strategy can quantificationally estimate the damage for imbalanced classification tasks and provide priori information to guide us to select appropriate classification methods. Moreover, the computing complexity of the proposed strategy is quite low, thus it is practical in real-world applications.
Keywords: Class imbalance, harmfulness, scatter matrix based class separability measure, imbalanced ratio, classification
