Affiliations: [a] GECAD, Instituto Superior de Engenharia do Porto, Porto, Portugal | [b] CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal | [c] Institute of Policy and Management, Chinese Academy of Sciences, Beijing, China
Correspondence:
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Corresponding author: GECAD, Instituto Superior de Engenharia do Porto, Rua Dr. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal. Tel.: +351 228340500; Fax: +351 228321159; E-mail: ningchen74@gmail.com.
Abstract: Skewed class distribution and non-uniform misclassification cost are pervasive in many real-world domains such as bankruptcy prediction, medical diagnosis, and intrusion detection. Although class imbalance learning and cost-sensitive learning can be manipulated in a unified framework as was illustrated in previous studies, the influence of class distribution on cost-sensitive learning still needs clarification. In this paper, we investigate the effect of cost ratio, imbalance ratio and sample size on classification performance using a real-world French bankruptcy database. The results show that the cost ratio and the level of class imbalance have strong effect on prediction performance. A near-balanced training data set is favorable when a relatively uniform cost ratio is used, whereas a near-natural class distribution is favorable when a highly uneven cost ratio is used.
