Li Wu: A Comparative Study of Unsupervised Anomaly Detection Methods: Isolation Forest and LOF

Abstract: This study compares two unsupervised anomaly detection methods: Isolation Forest and Local Outlier Factor (LOF). Experiments are conducted on two datasets with varying sizes, dimensionalities, and anomaly ratios. Performance is evaluated using Receiver Operating Characteristic (ROC) analysis and Precision-Recall (PR) analysis, together with an analysis of hyperparameter sensitivity and computational efficiency.

The results indicate that Isolation Forest provides a strong balance between accuracy and efficiency, making it a robust general-purpose method. In contrast, LOF performs well only when anomalies are locally separable and the data are low-dimensional, while being sensitive to parameter settings.