Each week, we select a recently published Open Access article to feature. This week’s article comes from Statistical Analysis and Data Mining and proposes a method called Bilateral-Weighted Online Adaptive Isolation Forest for anomaly detection. 

The article’s abstract is given below, with the full article freely available to read here.

Hannák, G., Horváth, G., Kádár, A., Szalai, M. D., Bilateral-Weighted Online Adaptive Isolation Forest for anomaly detection in streaming data, Stat. Anal. Data Min.: ASA Data Sci. J.. (2023), 1– 9. https://doi.org/10.1002/sam.11612

We propose a method called Bilateral-Weighted Online Adaptive Isolation Forest (BWOAIF) for unsupervised anomaly detection based on Isolation Forest (IF), which is applicable to streaming data and able to cope with concept drift. Similar to IF, the proposed method has only few hyperparameters whose effect on the performance are easy to interpret by human intuition and therefore easy to tune. BWOAIF ingests data and classifies it as normal or anomalous, and simultaneously adapts its classifier by removing old trees as well as by creating new ones. We show that BWOAIF adapts gradually to slow concept drifts, and, at the same time, it is able to adapt fast to sudden changes of the data distribution. Numerical results show the efficacy of the proposed algorithm and its ability to learn different classes of concept drifts, such as slow/fast concept shift, concept split, concept appearance, and concept disappearance.