Learning to Rank by Maximizing AUC with Linear Programming

  title={Learning to Rank by Maximizing AUC with Linear Programming},
  author={Kaan Ataman and William Nick Street and Yi Zhang},
  journal={The 2006 IEEE International Joint Conference on Neural Network Proceedings},
Area Under the ROC Curve (AUC) is often used to evaluate ranking performance in binary classification problems. Several researchers have approached AUC optimization by approximating the equivalent Wicoxon-Mann-Whitney (WMW) statistic. We present a linear programming approach similar to 1-norm Support Vector Machines (SVMs) for instance ranking by an approximation to the WMW statistic. Our formulation can be applied to nonlinear problems by using a kernel function. Our ranking algorithm… CONTINUE READING
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