Semi-Supervised Isolation Forest for Anomaly Detection

Anomaly detection algorithms attempt to find instances that deviate from the expected behavior. Because this is often tackled as an unsupervised task, anomaly detection models rely on exploiting intuitions about what constitutes anomalous behavior. These typically take the form of data-driven heuristics that measure the anomalousness of each instance. However, the effectiveness of unsupervised detectors are limited by the validity of their intuition. Because these are not universally true, one can improve the detectors' performance by using a semi-supervised approach that exploits a few labeled instances. This paper proposes a novel semi-supervised tree ensemble based anomaly detection framework. We compare our proposed approach to several baselines and show that it achieves comparable performance to state-of-the-art neural networks on six real-world and 14 benchmark datasets.