학술논문
Learning to Rank by Maximizing AUC with Linear Programming
Document Type
Conference
Author
Source
The 2006 IEEE International Joint Conference on Neural Network Proceedings Neural Networks, 2006. IJCNN '06. International Joint Conference on. :123-129 2006
Subject
Language
ISSN
2161-4393
2161-4407
2161-4407
Abstract
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 outperforms SVMs in both AUC and classification performance when using RBF kernels, but curiously not with polynomial kernels. We experiment with variations of chunking to handle the quadratic growth of the number of constraints in our formulation.