Enhanced anomaly scores for isolation forests

Abstract

Isolation Forest represents a variant of Random Forest largely and successfully employed for outlier detection. 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 employs a different aggregation function to combine the tree scores. We thoroughly evaluate the proposed methodology by testing it on sixteen datasets.