Enhancing Interpretability and Generalizability in Extended Isolation Forests

Anomaly Detection (AD) focuses on identifying unusual patterns in complex datasets and systems. While Machine Learning and Decision Support Systems (DSS) are effective for this, simply detecting anomalies often falls short in real-world scenarios, especially in engineering contexts where diagnostics and maintenance are essential. Users need clear explanations behind anomaly predictions to understand the root causes and trust the model. The unsupervised nature of AD complicates the development of interpretable tools. To address this, we propose the Extended Isolation Forest Feature Importance (ExIFFI), a new approach that explains the predictions of the Extended Isolation Forest (EIF), applicable to all Isolation Forest models that split using hyperplanes. ExIFFI provides both global and local explanations by analyzing feature importance. Additionally, we introduce Enhanced Extended Isolation Forest (EIF+), an improved version of EIF, designed to better detect unseen anomalies by modify...