A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Robust Classification for Imprecise Environments
Machine Learning
Learning Decision Trees Using the Area Under the ROC Curve
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Optimising area under the ROC curve using gradient descent
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
A ROC-based reject rule for dichotomizers
Pattern Recognition Letters
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Exploiting AUC for optimal linear combinations of dichotomizers
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Linear model combining by optimizing the Area under the ROC curve
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Margin-Based ranking meets boosting in the middle
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Score Fusion by Maximizing the Area under the ROC Curve
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Dynamic Score Combination: A Supervised and Unsupervised Score Combination Method
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Dynamic linear combination of two-class classifiers
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Obtaining optimal class distribution for decision trees: comparative analysis of CTC and C4.5
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
A dynamic over-sampling procedure based on sensitivity for multi-class problems
Pattern Recognition
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
An online AUC formulation for binary classification
Pattern Recognition
A two-stage evolutionary algorithm based on sensitivity and accuracy for multi-class problems
Information Sciences: an International Journal
Pattern Recognition
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The majority of the available classification systems focus on the minimization of the classification error rate. This is not always a suitable metric specially when dealing with two-class problems with skewed classes and cost distributions. In this case, an effective criterion to measure the quality of a decision rule is the area under the Receiver Operating Characteristic curve (AUC) that is also useful to measure the ranking quality of a classifier as required in many real applications. In this paper we propose a nonparametric linear classifier based on the maximization of AUC. The approach lies on the analysis of the Wilcoxon-Mann-Whitney statistic of each single feature and on an iterative pairwise coupling of the features for the optimization of the ranking of the combined feature. By the pairwise feature evaluation the proposed procedure is essentially different from other classifiers using AUC as a criterion. Experiments performed on synthetic and real data sets and comparisons with previous approaches confirm the effectiveness of the proposed method.