A comparative evaluation of outlier detection algorithms: Experiments and analyses

Rémi Domingues*, Maurizio Filippone, Pietro Michiardi, Jihane Zouaoui

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

349 Scopus citations


We survey unsupervised machine learning algorithms in the context of outlier detection. This task challenges state-of-the-art methods from a variety of research fields to applications including fraud detection, intrusion detection, medical diagnoses and data cleaning. The selected methods are benchmarked on publicly available datasets and novel industrial datasets. Each method is then submitted to extensive scalability, memory consumption and robustness tests in order to build a full overview of the algorithms’ characteristics.

Original languageEnglish (US)
Pages (from-to)406-421
Number of pages16
JournalPattern Recognition
StatePublished - Feb 2018

Bibliographical note

Publisher Copyright:
© 2017 Elsevier Ltd


  • Fraud detection
  • Isolation forest
  • Novelty detection
  • Outlier detection
  • Variational inference

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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