xAAD–Post-Feedback Explainability for Active Anomaly Discovery

xAAD–Post-Feedback Explainability for Active Anomaly Discovery

Anomaly detection algorithms are widely used across various domains, but they often suffer from high false positive rates and lack of interpretability. This paper introduces xAAD, a novel approach that combines Active Anomaly Discovery (AAD) with the Assist-Based Weighting Scheme (AWS) explainabilit...

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Bibliographic Details
Main Authors: Damir Kopljar, Vjekoslav Drvar, Jurica Babic, Vedran Podobnik
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Anomaly detection
isolation forest
semi-supervised learning
user feedback
active anomaly discovery
explainability
Online Access:https://ieeexplore.ieee.org/document/10772236/
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https://ieeexplore.ieee.org/document/10772236/

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