期刊
INTELLIGENT DATA ANALYSIS
卷 17, 期 3, 页码 423-437出版社
IOS PRESS
DOI: 10.3233/IDA-130587
关键词
Classification; non-uniform misclassification cost; class imbalance; cost-sensitive learning; bankruptcy prediction
资金
- Natural Science Foundation of China [91024004]
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.
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