Maximizing the area under the ROC curve by pairwise feature combination

[1]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[2]  Robert P. W. Duin,et al.  Linear model combining by optimizing the Area under the ROC curve , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[3]  William Nick Street,et al.  Learning to Rank by Maximizing AUC with Linear Programming , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[4]  Claudio Marrocco,et al.  Exploiting AUC for optimal linear combinations of dichotomizers , 2006, Pattern Recognit. Lett..

[5]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[6]  Cynthia Rudin,et al.  Margin-Based Ranking Meets Boosting in the Middle , 2005, COLT.

[7]  Charles X. Ling,et al.  Using AUC and accuracy in evaluating learning algorithms , 2005, IEEE Transactions on Knowledge and Data Engineering.

[8]  Francesco Tortorella,et al.  A ROC-based reject rule for dichotomizers , 2005, Pattern Recognit. Lett..

[9]  Ulf Brefeld,et al.  {AUC} maximizing support vector learning , 2005 .

[10]  Bhavani Raskutti,et al.  Optimising area under the ROC curve using gradient descent , 2004, ICML.

[11]  David J. Hand,et al.  A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems , 2001, Machine Learning.

[12]  Tom Fawcett,et al.  Robust Classification for Imprecise Environments , 2000, Machine Learning.

[13]  Alain Rakotomamonjy,et al.  Optimizing Area Under Roc Curve with SVMs , 2004, ROCAI.

[14]  Mehryar Mohri,et al.  AUC Optimization vs. Error Rate Minimization , 2003, NIPS.

[15]  Michael C. Mozer,et al.  Optimizing Classifier Performance via an Approximation to the Wilcoxon-Mann-Whitney Statistic , 2003, ICML.

[16]  Peter A. Flach The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics , 2003, ICML.

[17]  Peter A. Flach,et al.  Learning Decision Trees Using the Area Under the ROC Curve , 2002, ICML.

[18]  Robert P. W. Duin,et al.  Is independence good for combining classifiers? , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[19]  Yoram Singer,et al.  An Efficient Boosting Algorithm for Combining Preferences by , 2013 .

[20]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[21]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[22]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[23]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[24]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[25]  S. Holm A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[26]  E. Lehmann,et al.  Nonparametrics: Statistical Methods Based on Ranks , 1976 .

[27]  M. S. Bartlett,et al.  Statistical methods and scientific inference. , 1957 .

[28]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[29]  M. Friedman The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance , 1937 .