TY - GEN
T1 - Adaptive Isolation Forest
AU - Liu, Jia Justin
AU - Cassales, Guilherme Weigert
AU - Liu, Fei Tony
AU - Pfahringer, Bernhard
AU - Bifet, Albert
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - Anomaly detection in real-world data streams often struggles with concept drift, where evolving data distributions challenge algorithms to maintain a balance between accuracy, speed, stability, and plasticity. We present Adaptive Isolation Forest (AIF), a novel anomaly detection algorithm designed to effectively adapt to such changes in a resource-efficient and balanced manner. AIF’s novelty can be found in the combination of a smart model update mechanism together with a newly developed MinTreeMaxMass (MTMM) criterion, which scores individual trees for replacement. Extensive evaluations on various benchmark datasets demonstrate that AIF significantly outperforms existing state-of-the-art streaming anomaly detection algorithms in terms of detection accuracy. Moreover, AIF achieves linear time and space complexities, providing a robust solution that maintains high accuracy and efficiency, balancing stability and plasticity in dynamic data streams.
AB - Anomaly detection in real-world data streams often struggles with concept drift, where evolving data distributions challenge algorithms to maintain a balance between accuracy, speed, stability, and plasticity. We present Adaptive Isolation Forest (AIF), a novel anomaly detection algorithm designed to effectively adapt to such changes in a resource-efficient and balanced manner. AIF’s novelty can be found in the combination of a smart model update mechanism together with a newly developed MinTreeMaxMass (MTMM) criterion, which scores individual trees for replacement. Extensive evaluations on various benchmark datasets demonstrate that AIF significantly outperforms existing state-of-the-art streaming anomaly detection algorithms in terms of detection accuracy. Moreover, AIF achieves linear time and space complexities, providing a robust solution that maintains high accuracy and efficiency, balancing stability and plasticity in dynamic data streams.
KW - Anomaly Detection
KW - Ensembles
KW - Streaming Models
UR - https://www.scopus.com/pages/publications/105020024979
U2 - 10.1007/978-3-032-05461-6_24
DO - 10.1007/978-3-032-05461-6_24
M3 - Conference contribution
AN - SCOPUS:105020024979
SN - 9783032054609
T3 - Lecture Notes in Computer Science
SP - 363
EP - 378
BT - Discovery Science - 28th International Conference, DS 2025, Proceedings
A2 - Džeroski, Sašo
A2 - Levatic, Jurica
A2 - Pio, Gianvito
A2 - Simidjievski, Nikola
PB - Springer Science and Business Media Deutschland GmbH
T2 - 28th International Conference on Discovery Science, DS 2025
Y2 - 23 September 2025 through 25 September 2025
ER -