Rank Learning by Ordinal Gerrymandering
Stefano Fenu, Chris Rozell · 2017
Many applications, from ordering search engine results to medical triage, rely on learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. We propose a technique for rank-learning using a boosting approach which merges accurate regions of poor-quality metrics into a single accurate metric. We show an improvement in accuracy for general similarity-ranking tasks across a variety of benchmark datasets and apply this technique to the prediction of software bug severity and resolution time from error report text, showing a significant improvement in bug triage accuracy over the state of the art.