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The Influence of Class Imbalance on Cost-Sensitive Learning: An Empirical Study

, und . Data Mining, 2006. ICDM '06. Sixth International Conference on, Seite 970-974. (Dezember 2006)
DOI: 10.1109/ICDM.2006.158

Zusammenfassung

In real-world applications the number of examples in one class may overwhelm the other class, but the primary interest is usually on the minor class. Cost-sensitive learning has been deeded as a good solution to these class-imbalanced tasks, yet it is not clear how does the class-imbalance affect cost-sensitive classifiers. This paper presents an empirical study using 38 data sets, which discloses that class-imbalance often affects the performance of cost-sensitive classifiers: When the misclassification costs are not seriously unequal, cost-sensitive classifiers generally favor natural class distribution although it might be imbalanced; while when misclassification costs are seriously unequal, a balanced class distribution is more favorable.

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