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Nearest-Neighbor-Based Active Learning for Rare Category Detection Jingrui He, Jaime Carbonell

Nearest-Neighbor-Based Active Learning for Rare Category Detection Jingrui He, Jaime Carbonell. NO Labeled Examples NIPS are Rare Classes. How to Find NIPS?. Main Theorem : If 1) ;

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Nearest-Neighbor-Based Active Learning for Rare Category Detection Jingrui He, Jaime Carbonell

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  1. Nearest-Neighbor-Based Active Learning for Rare Category Detection Jingrui He, Jaime Carbonell • NO Labeled Examples • NIPS are Rare Classes How to Find NIPS? Main Theorem: If 1) ; 2) , , ; 3); with probability at least , after iterations, NNDB queries at least one example whose probability of coming from the minority class is at least 1/3, and it continues qu -erying such examples until the iteration. Bad News • Class Priors are Known • Majority Class is Smooth • Rare Classes are Clustered Good News • Fraud Detection • Astronomy • Network Intrusion Detection Applications Neighbor-Neighbor-Based Rare Category Detection Shuttle Dataset: Reducing the labeling effort of RS by 83% 1.Calculate class-specific radius • , , Increase t by 1 3. Abalone Dataset: Reducing the labeling effort of RS by 74% 4. Query rare class?

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