Extended Isolation Forest

About

We present an extension to the model-free anomaly detection algorithm, Isolation Forest. This extension, named Extended Isolation Forest (EIF), resolves issues with assignment of anomaly score to given data points. We motivate the problem using heat maps for anomaly scores. These maps suffer from artifacts generated by the criteria for branching operation of the binary tree. We explain this problem in detail and demonstrate the mechanism by which it occurs visually. We then propose two different approaches for improving the situation. First we propose transforming the data randomly before creation of each tree, which results in averaging out the bias. Second, which is the preferred way, is to allow the slicing of the data to use hyperplanes with random slopes. This approach results in remedying the artifact seen in the anomaly score heat maps. We show that the robustness of the algorithm is much improved using this method by looking at the variance of scores of data points distributed along constant level sets. We report AUROC and AUPRC for our synthetic datasets, along with real-world benchmark datasets. We find no appreciable difference in the rate of convergence nor in computation time between the standard Isolation Forest and EIF.

Sahand Hariri, Matias Carrasco Kind, Robert J. Brunner · 2018

Related benchmarks

Task	Dataset	Result
Anomaly Detection	SMD	F1 Score40	375
Time Series Anomaly Detection	GECCO	VUS-ROC0.69	97
Time Series Anomaly Detection	TSB-AD-M	VUS-PR21	83
Anomaly Detection	Shuttle	AUC0.843	61
Multivariate Time Series Anomaly Detection	PSM	Precision—	44
Time Series Anomaly Detection	PSM	Standard-F127	38
Anomaly Detection	COVER	AUC-PR0.04	34
Time Series Anomaly Detection	Daphnet	AUC-PR15	33
Time Series Anomaly Detection	MITDB	AUC-PR4	33
Time Series Anomaly Detection	SVDB	AUC-PR5	33