Handling classification errors by learning to reject classifications

Abstract

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%.