Sparse random projection isolation forest for outlier detection.

• We analyzed the isolation-forest-based methods' problem of lacking efficacy in selecting suitable hyperplanes to split data. • We proposed to use random projection techniques to improve isolation forest's efficacy in selecting suitable hyperplanes. • Experimental results on 30 real-world datasets show that the proposed methods outperform 12 SOTA outlier detectors. [Display omitted] Isolation Forest has a low computational complexity, hence has been widely applied to detect outliers in large-scale data. However, it suffers from the artifacts caused by the hyperplanes chosen, thereby failing to detect outliers in some specific regions. To tackle this problem, we propose the random-projection-based Isolation Forest, which works in two steps. First, we transform the data using the random projection technique. Then, we employ the Isolation Forest to identify outliers using the transformed data. Experimental results show that the proposed methods outperform 12 state-of-the-art outlier detectors. [ABSTRACT FROM AUTHOR]