Title: A quest for better anomaly detectors

Authors: Mehdi Soleymani

Addresses: Department of Statistics, Auckland University, Auckland, New Zealand

Abstract: 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.

Keywords: anomaly detection; algorithm; k-nearest neighbour.

DOI: 10.1504/IJDMMM.2020.111399

International Journal of Data Mining, Modelling and Management, 2020 Vol.12 No.4, pp.447 - 458

Received: 07 Mar 2019
Accepted: 03 Feb 2020
Published online: 25 Nov 2020 *

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