Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors

DOI10.1007/s10994-016-5586-4zbMath1412.68193OpenAlexW2507487910MaRDI QIDQ2360971

Sunil Aryal, Kai Ming Ting, Jonathan R. Wells, Takashi Washio

Publication date: 29 June 2017

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-016-5586-4

zbMATH Keywords

AUCcomputational geometrylearning curveanomaly detectionnearest neighbour

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10) Computer graphics; computational geometry (digital and algorithmic aspects) (68U05)

Related Items (1)

Homophily outlier detection in non-IID categorical data

Uses Software

• UCI-ml
• WEKA
• HandTill2001
• LOF
• LeSiNN
• Orca
• IsolationForest

Cites Work

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• LiNearN: a new approach to nearest neighbour density estimator
• Asymptotic moments of near–neighbour distance distributions
• A simple generalisation of the area under the ROC curve for multiple class classification problems

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