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Cost-Sensitive Stacking: an Empirical Evaluation
Many real-world classification problems are cost-sensitive in nature, such that the misclassification costs vary between data instances.
Misclassification cost-sensitive ensemble learning: A unifying framework
Over the years, a plethora of cost-sensitive methods have been proposed for learning on data when different types of misclassification errors incur different costs.
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