Extreme re-balancing for SVMs: a case study
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
An approach to mining the multi-relational imbalanced database
Expert Systems with Applications: An International Journal
A decision support system for detecting products missing from the shelf based on heuristic rules
Decision Support Systems
Improvement in intrusion detection with advances in sensor fusion
IEEE Transactions on Information Forensics and Security
A hybrid approach for Pap-Smear cell nucleus extraction
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Evaluation of rare event detection
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
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Predicting rare classes effectively is an important problem.The definition of effective classifier, embodied in theclassifier evaluation metric, is however very subjective, dependenton the application domain. In this paper, a widevariety of point-metrics are put into a common analyticalcontext defined by the recall and precision of the target rareclass. This enables us to compare various metrics in an objective,domain-independent manner. We judge their suitabilityfor the rare class problems along the dimensions oflearning difficulty and levels of rarity. This yields manyvaluable insights. In order to address the goal of achievingbetter recall and precision, we also propose a way ofcomparing classifiers directly based on the relationships betweenrecall and precision values. It resorts to a compositepoint-metric only when recall-precision based comparisonsyield conflicting results.