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Evolution of Space-Partitioning Forest for Anomaly Detection

  • Zhiruo ZhaoEmail author
  • Stuart W. Card
  • Kishan G. Mehrotra
  • Chilukuri K. Mohan
Conference paper
Part of the Genetic and Evolutionary Computation book series (GEVO)

Abstract

Previous work proposed a fast one-class anomaly detector using an ensemble of random half-space partitioning trees. The method was shown to be effective and efficient for detecting anomalies in streaming data. However, the parameters were pre-defined, so the random partitions of the data space might not be optimal. Therefore, the aims of this study were to: (a) give some mathematical analysis of the random partitioning trees; and (b) explore optimizing forests for anomaly detection using evolutionary algorithms.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Zhiruo Zhao
    • 1
    Email author
  • Stuart W. Card
    • 1
  • Kishan G. Mehrotra
    • 1
  • Chilukuri K. Mohan
    • 1
  1. 1.Syracuse UniversitySyracuseUSA

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