A quest for better anomaly detectors. (5th November 2020)

• Record Type: Journal Article
• Authors: Soleymani, Mehdi
• Is Part Of: International journal of data mining, modelling and management. Volume 12:Number 4(2020)
• Page Start: 447
• Page End: 458
• Publication Date: 2020-11-05
• Subjects: anomaly detection -- algorithm -- k-nearest neighbour; Data mining -- Periodicals; Information science -- Periodicals; Databases -- Periodicals; 005.7
• Languages: English
• ISSNs: 1759-1163

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.