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ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining with rarity: a unifying framework
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CCCS: a top-down associative classifier for imbalanced class distribution
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An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A review of associative classification mining
The Knowledge Engineering Review
Boosting text segmentation via progressive classification
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Concept learning and the problem of small disjuncts
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Data preparation techniques for improving rare class prediction
MAMECTIS/NOLASC/CONTROL/WAMUS'11 Proceedings of the 13th WSEAS international conference on mathematical methods, computational techniques and intelligent systems, and 10th WSEAS international conference on non-linear analysis, non-linear systems and chaos, and 7th WSEAS international conference on dynamical systems and control, and 11th WSEAS international conference on Wavelet analysis and multirate systems: recent researches in computational techniques, non-linear systems and control
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A hierarchical classification framework is proposed for discriminating rare classes in imprecise domains, characterized by rarity (of both classes and cases), noise and low class separability. The devised framework couples the rules of a rule-based classifier with as many local probabilistic generative models. These are trained over the coverage of the corresponding rules to better catch those globally rare cases/classes that become less rare in the coverage. Two novel schemes for tightly integrating rule-based and probabilistic classification are introduced, that classify unlabeled cases by considering multiple classifier rules as well as their local probabilistic counterparts. An intensive evaluation shows that the proposed framework is competitive and often superior in accuracy w.r.t. established competitors, while overcoming them in dealing with rare classes.