Duda R O, Hart P E, Stork D G. Pattern Classification. 2nd Edition. New York, USA: Wiley, 2001
[2]
Bradley A P. The Use of the Area under the ROC Curve in the Evaluation of Machine Learning Algorithms. Pattern Recognition, 1997, 30(7): 1145-1159
[3]
Provost F T, Fawcett T, Kohavi R. The Case against Accuracy Estimation for Comparing Induction Algorithms // Proc of the 15th International Conference on Machine Learning. Madison, USA, 1998: 445-453
[4]
Provost F, Fawcett T. Robust Classification for Imprecise Environments. Machine Learning, 2001, 42(3): 203-231
[5]
Ling C X, Huang Jin, Zhang H. AUC: A Better Measure than Accuracy in Comparing Learning Algorithms // Proc of the 16th Canadian Society for Computational Studies of Intelligence Conference on Advances in Artificial Intelligence. Halifax, Canada, 2003: 329-341
[6]
Ling C X, Huang Jin, Zhang H. AUC: A Statistically Consistent and More Discriminating Measure Than Accuracy // Proc of the 18th International Joint Conference on Artificial Intelligence. Acapulco, Mexico, 2003: 519-526
[7]
Maloof M A. Learning When Data Sets Are Imbalanced and When Costs Are Unequal and Unknown // Proc of the Workshop on Learning from Imbalanced Data Sets II. Washington, USA, 2003: 96-105
[8]
Akbani R, Kwek S, Japkowicz N. Applying Support Vector Machines to Imbalance Data Sets // Proc of the 15th European Conference on Machine Learning. Pisa, Italy, 2004: 39-50
[9]
Provost F, Fawcett T. Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost // Proc of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Jose, USA, 1997: 43-48
[10]
Huang Jin, Ling C X. Using AUC and Accuracy in Evaluating Learning Algorithms. IEEE Trans on Knowledge and Data Engineering, 2005, 17(3): 299-310
[11]
Yan Lian, Dodier R, Mozer M C, et al. Optimizing Classifier Performance via the Wilcoxon-Mann-Whitney Statistic // Proc of the 20th International Conference on Machine Learning. Washington, USA, 2003: 848-855
[12]
Freund Y, Iyer R, Schapire R E, et al. An Efficient Boosting Algorithm for Combining Preferences // Proc of the International Conference on Machine Learning. Madison, USA, 1998: 170-178
[13]
Herschtal A, Raskutti B. Optimizing Area under the ROC Curve Using Gradient Descent // Proc of the 21st International Conference on Machine Learning. Banff, Canada, 2004: 49-56
[14]
Rakotomamonjy A. Optimizing AUC with Support Vector Machine (SVM) // Proc of the European Conference on Artificial Intelligence Workshop on ROC Analysis and Artificial Intelligence. Valencia, Spain, 2004: 71-80
[15]
Rakotomamonjy A. Quadratic Programming for AUC Optimization // Proc of the 2nd International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences. Metz, France, 2004: 603-610
[16]
Fawcett T. An Introduction to ROC Analysis. Pattern Recognition Letters, 2006, 27(8): 861-874
[17]
Rosset S. Model Selection via the AUC // Proc of the 21st International Conference on Machine Learning. Banff, Canada, 2004: 89-97
[18]
Wu S, Flach P. A Scored AUC Metric for Classifier Evaluation and Selection // Proc of the ICML Workshop on ROC Analysis in Machine Learning. Bonn, Germany, 2005: 247-262
[19]
Hand D J, Till R J. A Simple Generalization of the Area under the ROC Curve for Multiple-Class Classification Problems. Machine Learning, 2001, 45(2): 171-186
[20]
Calders T, Jaroszewicz S. Efficient AUC Optimization for Classification // Proc of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases. Warsaw, Poland, 2007: 42-53
[21]
Burges C, Shaked T, Renshaw E, et al. Learning to Rank Using Gradient Descent // Proc of the 22nd International Conference on Machine Learning. Bonn, Germany, 2005: 89-96
[22]
Freund Y, Iyer R, Schapire R E, et al. An Efficient Boosting Algorithm for Combining Preferences. Journal of Machine Learning Research, 2003, 4: 933-969
[23]
Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge, UK: Cambridge University Press, 2000
[24]
Lin Yi, Lee Y, Wahba G. Support Vector Machines for Classification in Nonstandard Situations. Machine Learning, 2002, 46(1/2/3): 191-202
[25]
Fumera G, Roli F. Cost-Sensitive Learning in Support Vector Machine // Proc of the Workshop on Machine Learning, Methods and Applications. Siena, Italy, 2002: 147-167
[26]
Brefeld U, Scheffer T. AUC Maximizing Support Vector Learning // Proc of the ICML Workshop on ROC Analysis in Machine Learning. Bonn, Germany, 2005: 377-384
[27]
Joachims T. Optimizing Search Engines Using Clickthrough Data // Proc of the ACM Conference on Knowledge Discovery and Data Mining. Edmonton. Canada, 2002: 133-142
[28]
Wang Yunyun, Chen Songcan, Xue Hui. Structure-Embedded AUC-SVM. International Journal of Pattern Recognition and Artificial Intelligence, 2010, 24(5): 667-690
[29]
Fung G, Rosales R O, Krishnapuram B. Learning Rankings via Convex Hull Separation // Proc of the 19th Annual Conference on Neural Information Processing Systems. Vancouver, Canada, 2005: 395-402
[30]
Yan Rong, Hauptmann A G. Efficient Margin-Based Rank Learning Algorithms for Information Retrieval // Proc of the International Conference on Image and Video Retrieval. Tempe, USA, 2006: 113-122
[31]
Tax D M J, Veenman C J. Tuning the Hyperparameter of an AUC-Optimized Classifier // Proc of the 17th Belgium-Netherlands Conference on Artificial Intelligence. Brussels, Belgium, 2005: 224-231
[32]
Tax D M J, Duin R P W. Linear Model Combining by Optimizing the Area under the ROC Curve // Proc of the 18th International Conference on Pattern Recognition. Hongkong, China, 2006: 119-122
[33]
Ataman K, Street W N, Zhang Y. Learning to Rank by Maximizing AUC with Linear Programming // Proc of the IEEE International Joint Conference on Neural Networks. Vancouver. Canada, 2006: 123-129
[34]
Ataman K. Learning to Rank by Maximizing the AUC with Linear Programming for Problems with Binary Output. Ph.D Dissertation. Iowa, USA: University of Iowa. Department of Management Sciences, 2007
[35]
Yu H, Kim Y, Hwang S. RV-SVM: An Efficient Method for Learning Ranking SVM // Proc of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. Bangkok, Thailand, 2009: 426-438
[36]
Ferri C, Flach P, Hernández-Orallo J, et al. Modifying ROC Curves to Incorporate Predicted Probabilities // Proc of the ICML Workshop on ROC Analysis in Machine Learning. Bonn, Germany, 2005: 30-40
[37]
Raykar V C, Duraiswami R, Krishnapuram B. A Fast Algorithm for Learning a Ranking Function from Large Scale Data Sets. IEEE Trans on Pattern Analysis and Machine Intelligence, 2008, 30(7): 1158-1170
[38]
Raykar V C, Duraiswami R, Krishnapuram B. A Fast Algorithm for Learning Large Scale Preference Relations // Proc of the 11th International Conference on Artificial Intelligence and Statistics. San Juan, Puerto Rico, 2007: 385-392
[39]
Pahikkala T, Airola A, Suominen H, et al. Efficient AUC Maximization with Regularized Least-Squares // Proc of the 10th Scandinavian Conference on Artificial Intelligence. Stockholm City, Sweden, 2008: 12-19
[40]
Pahikkala T, Tsivtsivadze E, Airola A, et al. An Efficient Algorithm for Learning to Rank from Preference Graphs. Machine Learning, 2009, 75(1): 129-165
[41]
Toh K A, Kim J, Lee S. Maximizing Area under ROC Curve for Biometric Scores Fusion. Pattern Recognition, 2008, 41(11): 3373-3392
[42]
Rudin C, Schapire R E. Margin-Based Ranking and an Equivalence between AdaBoost and RankBoost. Journal of Machine Learning Research, 2008, 10: 2193-2232
[43]
Rudin C, Cortes C, Mohri M, et al. Margin-Based Ranking Meets Boosting in the Middle // Proc of the 18th Annual Conference on Computational Learning Theory. Bertinoro, Italy, 2005: 63-78
[44]
Moribe J I, Hatano K, Takimoto E, et al. Smooth Boosting for Margin-Based Ranking // Proc of the 19th International Conference on Algorithmic Learning Theory. Budapest, Hungary, 2008: 227-239
[45]
Pelckmans K, Suykens J A K. LPRankBoost and Column Generation // Proc of the International Conference/Euro Mini Conference on Continuous Optimization and Knowledge-Based Technologies. Neringa, Lithuania, 2008: 165-169
[46]
Merler M, Yan R, Smith J R. Imbalanced RankBoost for Efficiently Ranking Large-Scale Image/Video Collections // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA, 2009: 2607-2614
[47]
Warmuth M, Glocer K, Rtsch G. Boosting Algorithms for Maximizing the Soft Margin // Proc of the 22nd Annual Conference on Neural Information Processing Systems. Vancouver, Canada, 2008: 1441-1448
[48]
Tsai M F, Liu T Y, Qin Tao, et al. FRank: A Ranking Method with Fidelity Loss // Proc of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Amsterdam, Netherlands, 2007: 383-390
[49]
Pahikkala T, Tsivtsivadze E, Airola A, et al. Learning to Rank with Pairwise Regularized Least-Squares // Proc of the SIGIR Workshop on Learning to Rank for Information Retrieval. Amsterdam, Netherlands, 2007: 27-33
[50]
Tong Zong. Statistical Analysis of Some Multi-Category Large Margin Classification Methods. Journal of Machine Learning Research, 2004, 5: 1225-1251
[51]
Rosasco L, Vito E D, Caponnetto A, et al. Are Loss Function All the Same? Neural Computation, 2004, 16(5): 1063-1076
[52]
Nguyen X, Wainwright M J, Jordan M I. On Surrogate Loss Functions and f-Divergences. The Annals of Statistics, 2009, 37(2): 876-904
[53]
Steck H. Hinge Rank Loss and the Area under the ROC Curve // Proc of the 18th European Conference on Machine Learning. Warsaw, Poland, 2007: 347-358
[54]
Huang Jin, Ling C X. Constructing New and Better Evaluation Measures for Machine Learning // Proc of the 20th International Joint Conference on Artificial Intelligence. Hyderabad, India, 2007: 859-864
[55]
Vanderlooy S, Hüllermeier E. A Critical Analysis of Variants of the AUC. Machine Learning, 2008, 72(3): 247-262
[56]
Hand D J. Measuring Classifier Performance: A Coherent Alternative to the Area under the ROC Curve. Machine Learning, 2009, 77(1): 103-123
