An Optimized Computational Framework for Isolation Forest

Abstract

Isolation Forest or iForest is one of the outstanding outlier detectors proposed in recent years. Yet, in the model setting, it is mainly based on the technique of randomization and, as a result, it is not clear how to select a proper attribute and how to locate an optimized split point on a given attribute while building the isolation tree. Aiming to the two issues, we propose an improved computational framework which allows us to seek the most separable attributes and spot corresponding optimized split points effectively. According to the experimental results, the proposed model is able to achieve overall better performance in the accuracy of outlier detection compared with the original model and its related variants.