A quest for better anomaly detectors

被引:0
作者
Soleymani, Mehdi [1 ]
机构
[1] Auckland Univ, Dept Stat, Auckland, New Zealand
关键词
anomaly detection; algorithm; k-nearest neighbour; OUTLIERS;
D O I
10.1504/IJDMMM.2020.111399
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Anomaly detection is a very popular method for detecting exceptional observations which are very rare. It has been frequently used in medical diagnosis, fraud detection, etc. In this article, we revisit some popular algorithms for anomaly detection and investigate why we are on a quest for a better algorithm for identifying anomalies. We propose a new algorithm, which unlike other popular algorithms, is not looking for outliers directly, but it searches for them by removing the inliers (opposite to outliers) in an iterative way. We present an extensive simulation study to show the performance of the proposed algorithm compared to its competitors.
引用
收藏
页码:447 / 458
页数:12
相关论文
共 15 条
[1]  
[Anonymous], 2008, P 14 ACM SIGKDD INT
[2]  
[Anonymous], 2018, REV DATA MIN KNOWL D, DOI DOI 10.1002/WIDM.123.
[3]  
Breunig M.M., 2000, SIGMOD P ACM SIGMOD, P1
[4]  
Devroye L., 2015, Springer Series in the Data Sciences
[5]  
Dimitris P., 2018, ENCY DATABASE SYSTEM, V2nd
[6]   Rank-based outlier detection [J].
Huang, Huaming ;
Mehrotra, Kishan ;
Mohan, Chilukuri K. .
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2013, 83 (03) :518-531
[7]  
Jin W, 2006, LECT NOTES ARTIF INT, V3918, P577
[8]   Distance-based outliers: algorithms and applications [J].
Knorr, EM ;
Ng, RT ;
Tucakov, V .
VLDB JOURNAL, 2000, 8 (3-4) :237-253
[9]   Efficient Outlier Detection for High-Dimensional Data [J].
Liu, Huawen ;
Li, Xuelong ;
Li, Jiuyong ;
Zhang, Shichao .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2018, 48 (12) :2451-2461
[10]   Reverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection [J].
Radovanovic, Milos ;
Nanopoulos, Alexandros ;
Ivanovic, Mirjana .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2015, 27 (05) :1369-1382