ExIFFI and EIF+: Interpretability and Enhanced Generalizability to Extend the Extended Isolation Forest

arXiv:2310.05468v2 Announce Type: replace-cross
Abstract: Anomaly Detection involves identifying unusual behaviors within complex datasets and systems. While Machine Learning algorithms and Decision Support Systems (DSSs) offer effective solutions for this task, simply pinpointing anomalies may prove insufficient in real-world applications. Users require insights into the rationale behind these predictions to facilitate root cause analysis and foster trust in the model. However, the unsupervised nature of AD presents a challenge in developing interpretable tools. This paper addresses this challenge by introducing …

abstract algorithms anomaly anomaly detection applications arxiv cs.lg datasets decision decision support detection insights interpretability machine machine learning machine learning algorithms prove solutions stat.ap stat.ml support systems type world