The nature of statistical learning theory
The nature of statistical learning theory
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Readings in information visualization: using vision to think
Readings in information visualization: using vision to think
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Decision Trees Using the Area Under the ROC Curve
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
AUC: a better measure than accuracy in comparing learning algorithms
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
An empirical boosting scheme for ROC-based genetic programming classifiers
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
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ROC curves have been used for a fair comparison of machinelearning algorithms since the late 90's. Accordingly,the area under the ROC curve (AUC) is nowadays considereda relevant learning criterion, accommodating imbalanceddata, misclassification costs and noisy data.This paper shows how a genetic algorithm-based optimizationof the AUC criterion can be exploited for impactstudies and sensitivity analysis.The approach is illustrated on the Atherosclerosis Identificationproblem, PKDD 2002 Challenge.