Improving iForest with Relative Mass

  • Sunil Aryal
  • Kai Ming Ting
  • Jonathan R. Wells
  • Takashi Washio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8444)

Abstract

iForest uses a collection of isolation trees to detect anomalies. While it is effective in detecting global anomalies, it fails to detect local anomalies in data sets having multiple clusters of normal instances because the local anomalies are masked by normal clusters of similar density and they become less susceptible to isolation. In this paper, we propose a very simple but effective solution to overcome this limitation by replacing the global ranking measure based on path length with a local ranking measure based on relative mass that takes local data distribution into consideration. We demonstrate the utility of relative mass by improving the task specific performance of iForest in anomaly detection and information retrieval tasks.

Keywords

Relative mass iForest ReFeat anomaly detection 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sunil Aryal
    • 1
  • Kai Ming Ting
    • 2
  • Jonathan R. Wells
    • 1
  • Takashi Washio
    • 3
  1. 1.Monash UniversityAustralia
  2. 2.Federation UniversityAustralia
  3. 3.Osaka UniversityOsakaJapan

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