Abstract
• We propose a new unsupervised anomaly detection (AD) algorithm. • This algorithm is based on isolation forest with random hyperplanes instead of random dimensions. • The proposed method improves the existing extended isolation forest (EIF) in terms of computation time. This letter introduces a generalization of Isolation Forest (IF) based on the existing Extended IF (EIF). EIF has shown some interest compared to IF being for instance more robust to some artefacts . However, some information can be lost when computing the EIF trees since the sampled threshold might lead to empty branches. This letter introduces a generalized isolation forest algorithm called Generalized IF (GIF) to overcome these issues. GIF is faster than EIF with a similar performance, as shown in several simulation results associated with reference databases used for anomaly detection.
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