Generalized isolation forest for anomaly detection

This letter introduces a generalization of Isolation Forest (IF) based on the existing Extended IF (EIF). EIF has shown some interest compared to IF being for instance more robust to some artefacts. However, some information can be lost when computing the EIF trees since the sampled threshold might lead to empty branches. This letter introduces a generalized isolation forest algorithm called Generalized IF (GIF) to overcome these issues. GIF is faster than EIF with a similar performance, as shown in several simulation results associated with reference databases used for anomaly detection.

• Publication year

  2021

• Venue

  Pattern Recognition Letters

• Publication date

  2021-06-01

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1016/j.patrec.2021.05.022
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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