The research introduces a novel approach, Depth-based Extended Isolation Forest Feature Importance (DEIFFI), to enhance the interpretability of Extended Isolation Forest (EIF) algorithm in anomaly detection (AD). Anomaly detection is critical for identifying rare and significant deviations from norm in data. However, understanding the reasons behind classifying instances as anomalies poses a challenge. DEIFFI addresses this challenge by providing valuable insights, empowering users of EIF-based AD to conduct thorough root cause analysis. A noteworthy feature of DEIFFI is its capacity to improve interpretability without imposing heavy computational burdens. This is crucial for real world applications requiring efficient AD, particularly in situations demanding real-time decision-making. DEIFFI achieves remarkable results with low computational costs, making it an appealing option for practical implementations. With an accuracy of 0.914 and 0.942, precision of 0.607 and 0.64, recall of 0.773 and 0.96, and an F1 score of 0.68 and 0.768 on real and synthetic datasets, respectively. DEIFFI provides interpretable insights alongside competitive performance metrics, solidifying its suitability for real-time decision support. Importantly, DEIFFI contributes to AD by enhancing interpretability and assisting in unsupervised feature selection. This dual capability highlights practical utility of DEIFFI, improving EIF’s capabilities and extending its applicability across diverse AD scenarios.

Anomaly Detection, Explainable Artificial Intelligence, Extended Isolation Forest, Feature Selection, Interpretability, Outlier Detection

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