Isolation Forest

@article{Liu2008IsolationF,
  title={Isolation Forest},
  author={F. Liu and K. Ting and Z. Zhou},
  journal={2008 Eighth IEEE International Conference on Data Mining},
  year={2008},
  pages={413-422}
}
  • F. Liu, K. Ting, Z. Zhou
  • Published 2008
  • Computer Science
  • 2008 Eighth IEEE International Conference on Data Mining
  • Most existing model-based approaches to anomaly detection construct a profile of normal instances, then identify instances that do not conform to the normal profile as anomalies. [...] Key Method The use of isolation enables the proposed method, iForest, to exploit sub-sampling to an extent that is not feasible in existing methods, creating an algorithm which has a linear time complexity with a low constant and a low memory requirement. Our empirical evaluation shows that iForest performs favourably to ORCA, a…Expand Abstract
    Isolation-Based Anomaly Detection
    • 435
    • PDF
    Systematic construction of anomaly detection benchmarks from real data
    • 108
    • PDF
    Anomaly Detection using One-Class Neural Networks
    • 114
    • PDF
    Fast Anomaly Detection for Streaming Data
    • 111
    • PDF
    Anomaly Detection Using an Ensemble of Feature Models
    • 33
    • PDF
    Outlier Analysis
    • 772
    • Highly Influenced
    • PDF
    How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?
    • 22
    • Highly Influenced
    • PDF
    Incorporating Expert Feedback into Active Anomaly Discovery
    • 65
    • PDF

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 19 REFERENCES
    LOF: identifying density-based local outliers
    • 3,133
    • PDF
    Outlier detection by active learning
    • 270
    Algorithms for Mining Distance-Based Outliers in Large Datasets
    • 1,575
    • PDF
    Outlier detection by sampling with accuracy guarantees
    • 79
    • PDF
    Discovering cluster-based local outliers
    • 541
    • PDF