Isolation-Based Anomaly Detection

@article{Liu2012IsolationBasedAD,
  title={Isolation-Based Anomaly Detection},
  author={F. Liu and K. Ting and Z. Zhou},
  journal={ACM Trans. Knowl. Discov. Data},
  year={2012},
  volume={6},
  pages={3:1-3:39}
}
  • F. Liu, K. Ting, Z. Zhou
  • Published 2012
  • Computer Science
  • ACM Trans. Knowl. Discov. Data
  • Anomalies are data points that are few and different. [...] Key Result Our empirical evaluation shows that iForest outperforms ORCA, one-class SVM, LOF and Random Forests in terms of AUC, processing time, and it is robust against masking and swamping effects. iForest also works well in high dimensional problems containing a large number of irrelevant attributes, and when anomalies are not available in training sample.Expand Abstract
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