“Whoa! It's like Spotify but for academic articles.”

Instant Access to Thousands of Journals for just $40/month

Get 2 Weeks Free

Isolation-Based Anomaly Detection

Isolation-Based Anomaly Detection FEI TONY LIU and KAI MING TING, Monash University ZHI-HUA ZHOU, Nanjing University Anomalies are data points that are few and different. As a result of these properties, we show that, anomalies are susceptible to a mechanism called isolation. This article proposes a method called Isolation Forest (iForest), which detects anomalies purely based on the concept of isolation without employing any distance or density measure ”fundamentally different from all existing methods. As a result, iForest is able to exploit subsampling (i) to achieve a low linear time-complexity and a small memory-requirement and (ii) to deal with the effects of swamping and masking effectively. 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. Categories and Subject Descriptors: H.2.8 [Database Management]: Database Applications ”Data mining; I.2.6 [Arti cial Intelligence]: Learning General Terms: Algorithms, Design, Experimentation Additional Key Words and Phrases: Anomaly detection, outlier detection, ensemble methods, binary tree, random tree ensemble, isolation, isolation http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Knowledge Discovery from Data (TKDD) Association for Computing Machinery

Loading next page...

You're reading a free preview. Subscribe to read the entire article.

And millions more from thousands of peer-reviewed journals, for just $40/month

Get 2 Weeks Free

To be the best researcher, you need access to the best research

  • With DeepDyve, you can stop worrying about how much articles cost, or if it's too much hassle to order — it's all at your fingertips. Your research is important and deserves the top content.
  • Read from thousands of the leading scholarly journals from Springer, Elsevier, Nature, IEEE, Wiley-Blackwell and more.
  • All the latest content is available, no embargo periods.

Stop missing out on the latest updates in your field

  • We’ll send you automatic email updates on the keywords and journals you tell us are most important to you.
  • There is a lot of content out there, so we help you sift through it and stay organized.