Rank Learning by Ordinal Gerrymandering (bibtex)
by and
Abstract:
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 that operates by boosting, merging 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.
Reference:
Rank Learning by Ordinal GerrymanderingS. Fenu and C.J. Rozell. In Proceedings of the IEEE International Conference On Machine Learning And Applications (ICMLA), December 2017.
Bibtex Entry:
@INPROCEEDINGS{fenu.17,
  author = {Fenu, S. and Rozell, C.J.},
  title = {Rank Learning by Ordinal Gerrymandering},
  booktitle = {{Proceedings of the IEEE International Conference On Machine Learning And Applications (ICMLA)}},
  year = {2017},
  address = {Cancun, Mexico},
  month = dec,
  abstract = {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 that operates by boosting, merging 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.},
  url = {http://ieeexplore.ieee.org/abstract/document/8260792/}
  }
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