Ranking Instances by Maximizing the Area under ROC Curve

@article{Gvenir2013RankingIB,
  title={Ranking Instances by Maximizing the Area under ROC Curve},
  author={H. Altay G{\"u}venir and Murat Kurtcephe},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2013},
  volume={25},
  pages={2356-2366}
}
In recent years, the problem of learning a real-valued function that induces a ranking over an instance space has gained importance in machine learning literature. Here, we propose a supervised algorithm that learns a ranking function, called ranking instances by maximizing the area under the ROC curve (RIMARC). Since the area under the ROC curve (AUC) is a widely accepted performance measure for evaluating the quality of ranking, the algorithm aims to maximize the AUC value directly. For a… CONTINUE READING
11 Citations
50 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 11 extracted citations

Similar Papers

Loading similar papers…