Isolation Forest represents a variant of Random Forest largely and successfully employed for outlier de- tection. The main idea is that outliers are likely to get isolated in a tree after few splits. The anomaly score is therefore a function inversely related to the leaf depth. This paper proposes enhanced anomaly scores of the Isolation Forest by making two different contributions. The first consists in weighing the path traversed by an object to obtain a more informative anomaly score. The second contribution em- ploys a different aggregation function to combine the tree scores. We thoroughly evaluate the proposed methodology by testing it on sixteen datasets.

Enhanced anomaly scores for isolation forests

Antonella Mensi;Manuele Bicego
2021-01-01

Abstract

Isolation Forest represents a variant of Random Forest largely and successfully employed for outlier de- tection. The main idea is that outliers are likely to get isolated in a tree after few splits. The anomaly score is therefore a function inversely related to the leaf depth. This paper proposes enhanced anomaly scores of the Isolation Forest by making two different contributions. The first consists in weighing the path traversed by an object to obtain a more informative anomaly score. The second contribution em- ploys a different aggregation function to combine the tree scores. We thoroughly evaluate the proposed methodology by testing it on sixteen datasets.
2021
anomalty detection
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1086791
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