Open AccessRMHSForest: Relative Mass and Half‐Space Tree Based Forest for Anomaly Detection
Author(s) -
Lyu Yanxia,
Li Wenjie,
Wang Yue,
Sun Siqi,
Wang Cuirong
Publication year - 2020
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2020.09.010
Subject(s) - anomaly detection , anomaly (physics) , cluster analysis , tree (set theory) , set (abstract data type) , density estimation , mathematics , pattern recognition (psychology) , data set , computer science , data mining , artificial intelligence , statistics , physics , combinatorics , condensed matter physics , estimator , programming language
Anomaly detection refers to identify the true anomalies from a given data set. We present an ensemble anomaly detection method called Relative mass and half‐space tree based forest (RMHSForest), which detect anomalies, including global and local anomalies, based on relative mass estimation and halfspace tree. Different from density or distance based measure, RMHSForest utilizes a novel relative mass estimation to improve the detection of local anomaly. Meanwhile, half‐space tree based on augmented mass can estimate a mass distribution efficiently without density or distance calculations or clustering. Our empirical results show that RMHSForest outperforms the current popular anomaly detection algorithms in terms of AUC and processing time in the test data sets.