The Functional Isolation Forest, utilizing functional isolation trees (F-itrees), constructs anomaly scores based on the average depth of each observation across randomly built trees.
The selection of an appropriate dictionary D, composed of both deterministic and stochastic functions, is crucial in shaping the anomaly score construction in Functional Isolation Forests.
K-SIF shows significant advantages over traditional FIF, particularly in capturing the intricacies of real-world data anomalies, leading to improved detection performance.
Numerical experiments demonstrate that the Signature Isolation Forest method outperforms existing techniques in real-data anomaly detection benchmarks, showcasing its robust capabilities.
#anomaly-detection #functional-isolation-forest #numerical-experiments #statistical-methods #machine-learning
Collection
[
|
...
]