Cost-sensitive classifier evaluation
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Minimax Regret Classifier for Imprecise Class Distributions
The Journal of Machine Learning Research
Rule Extraction from Support Vector Machines: A Sequential Covering Approach
IEEE Transactions on Knowledge and Data Engineering
Classification algorithm sensitivity to training data with non representative attribute noise
Decision Support Systems
Improving Classification under Changes in Class and Within-Class Distributions
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
A sorting optimization curve with quality and yield requirements
Pattern Recognition Letters
Area under the ROC curve by bubble-sort approach (BSA)
ACMOS'05 Proceedings of the 7th WSEAS international conference on Automatic control, modeling and simulation
A unifying view on dataset shift in classification
Pattern Recognition
Drift mining in data: A framework for addressing drift in classification
Computational Statistics & Data Analysis
ROC analysis of classifiers in machine learning: A survey
Intelligent Data Analysis
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We counsel caution in the application of ROC analysis for prediction of classifier performance under varying class distributions. We argue that it is not reasonable to expect ROC analysis to provide accurate prediction of model performance under varying distributions if the classes contain causally relevant subclasses whose frequencies may vary at different rates or if there are attributes upon which the classes are causally dependent.