Robust classification systems for imprecise environments
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Explicitly representing expected cost: an alternative to ROC representation
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Robust Classification for Imprecise Environments
Machine Learning
Data Mining and Knowledge Discovery
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
The foundations of cost-sensitive learning
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When Overlapping Unexpectedly Alters the Class Imbalance Effects
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
A sorting optimization curve with quality and yield requirements
Pattern Recognition Letters
Cost-sensitive classifier evaluation using cost curves
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Performance evaluation of a fusion system devoted to image interpretation
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Information, Divergence and Risk for Binary Experiments
The Journal of Machine Learning Research
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ROC analysis of classifiers in machine learning: A survey
Intelligent Data Analysis
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Receiver operating characteristics (ROC) graphs are useful for organizing classifiers and visualizing their performance. ROC graphs have been used in cost-sensitive learning because of the ease with which class skew and error cost information can be applied to them to yield cost-sensitive decisions. However, they have been criticized because of their inability to handle instance-varying costs; that is, domains in which error costs vary from one instance to another. This paper presents and investigates a technique for adapting ROC graphs for use with domains in which misclassification costs vary within the instance population. pulation.