Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
ROC graphs with instance-varying costs
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Precision-recall operating characteristic (P-ROC) curves in imprecise environments
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
A New Performance Evaluation Method for Two-Class Imbalanced Problems
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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This paper makes use of several performance metrics to extend the understanding of a challenging imbalanced classification task. More specifically, we refer to a problem in which the minority class is more represented in the overlap region than the majority class, that is, the overall minority class becomes the majority one in this region. The experimental results demonstrate that the use of a set of appropriate performance measures allows to figure out such an atypical case.