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This work investigates how to improve classification metrics by learning when a classifier has higher chances of misclassification. The rejection technique increases the classification reliability and reduces the costs associated with misclassifications on cost-sensitive scenarios. A scenario of bug triaging classification shows the approach effectiveness where classification accuracy is increased from 67% up to 76%.

Handling classification errors by learning to reject classifications