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Isolation forest

15 Dec 2008archive 2025-07-28

Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou

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. This paper proposes a fundamentally different model-based method that explicitly isolates anomalies instead of profiles normal points. To our best knowledge, the concept of isolation has not been explored in current literature. 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 near-linear time complexity distance-based method, LOF and Random Forests in terms of AUC and processing time, and especially in large data sets. iForest also works well in high dimensional problems which have a large number of irrelevant attributes, and in situations where training set does not contain any anomalies.

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Tasks

Anomaly DetectionUnsupervised Anomaly Detection with Specified Settings -- 0.1% anomalyUnsupervised Anomaly Detection with Specified Settings -- 1% anomalyUnsupervised Anomaly Detection with Specified Settings -- 10% anomalyUnsupervised Anomaly Detection with Specified Settings -- 20% anomalyUnsupervised Anomaly Detection with Specified Settings -- 30% anomaly

Datasets

Introduced by this paper, per the archive.

ionosphere

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly CIFAR-10 Isolation Forest AUC-ROC 0.894 #3 of 6 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly Cats and Dogs Isolation Forest AUC-ROC 0.777 #5 of 6 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly Fashion-MNIST Isolation Forest AUC-ROC 0.908 #1 of 5 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly MNIST Isolation Forest AUC-ROC 0.777 #3 of 5 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly STL-10 Isolation Forest AUC-ROC 0.890 #3 of 6 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 1% anomaly CIFAR-10 Isolation Forest AUC-ROC 0.876 #3 of 6 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 1% anomaly Cats and Dogs IF AUC-ROC 0.878 #3 of 5 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 1% anomaly Fashion-MNIST Isolation Forest AUC-ROC 0.917 #1 of 5 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 1% anomaly MNIST Isolation Forest AUC-ROC 0.846 #4 of 5 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 1% anomaly STL-10 Isolation Forest AUC-ROC 0.917 #4 of 6 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly CIFAR-10 IF AUC-ROC 0.786 #5 of 6 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly Cats and Dogs IF AUC-ROC 0.798 #6 of 6 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly Fashion-MNIST IF AUC-ROC 0.915 #1 of 5 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly MNIST IF AUC-ROC 0.821 #4 of 5 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly STL-10 IF AUC-ROC 0.797 #6 of 6 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 20% anomaly Cats and Dogs IF AUC-ROC 0.706 #6 of 6 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 20% anomaly Fashion-MNIST IF AUC-ROC 0.889 #1 of 5 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 20% anomaly STL-10 Isolation Forest AUC-ROC 0.718 #6 of 6 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 20% anomaly cifar10 Isolation Forest AUC-ROC 0.721 #5 of 6 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 30% anomaly ASSIRA Cat Vs Dog Isolation Forest AUC-ROC 0.690 #5 of 6 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 30% anomaly CIFAR-10 IF AUC-ROC 0.661 #6 of 6 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 30% anomaly Fashion-MNIST IF AUC-ROC 0.889 #1 of 5 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 30% anomaly MNIST IF AUC-ROC 0.797 #3 of 5 Archive leaderboard report
Unsupervised Anomaly Detection with Specified Settings -- 30% anomaly STL-10 Isolation Forest AUC-ROC 0.638 #6 of 6 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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