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| Titel: |
Influence of class distribution on cost-sensitive learning: A case study of bankruptcy analysis |
| Auteur: |
Chen, Ning Chen, An Ribeiro, Bernardete |
| Verschenen in: |
Intelligent data analysis |
| Paginering: | Jaargang 17 (2013) nr. 3 pagina's 423-437 |
| Jaar: |
2013-05-21 |
| Inhoud: |
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. |
| Uitgever: |
IOS Press |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 6 van 10 gevonden artikelen
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