Discrete Rotated Isolation Forest in High Dimensions

Monemizadeh, Vahideh; Kiani, Kourosh

doi:10.22044/jadm.2025.15883.2704

Document Type : Original/Review Paper

Authors

Electrical and Computer Engineering Department, Semnan University, Semnan, Iran.

10.22044/jadm.2025.15883.2704

Abstract

Anomaly detection is becoming increasingly crucial across various fields, including cybersecurity, financial risk management, and health monitoring. However, it faces significant challenges when dealing with large-scale, high-dimensional, and unlabeled datasets. This study focuses on decision tree-based methods for anomaly detection due to their scalability, interpretability, and effectiveness in managing high-dimensional data. Although Isolation Forest (iForest) and its extended variant, Extended Isolation Forest (EIF), are widely used, they exhibit limitations in identifying anomalies, particularly in handling normal data distributions and preventing the formation of ghost clusters. The Rotated Isolation Forest (RIF) was developed to address these challenges, enhancing the model's ability to discern true anomalies from normal variations by employing randomized rotations in feature space. Building on this approach, we proposed the Discrete Rotated Isolation Forest (DRIF) model, which integrates an Autoencoder for dimensionality reduction. Using a discrete probability distribution and an Autoencoder enhance computational efficiency. Experimental evaluations on synthetic and real-world datasets demonstrate that proposed model outperforms iForest, EIF, and RIF. And also achieving higher Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) scores and significantly faster execution times. These findings establish the proposed model as a robust, scalable, and efficient approach for unsupervised anomaly detection in high-dimensional datasets.

Keywords

Main Subjects

H.3. Artificial Intelligence

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Discrete Rotated Isolation Forest in High Dimensions

References

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Volume 13, Issue 3July 2025Pages 347-358

Volume 13, Issue 3
July 2025
Pages 347-358