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Explainable Autoencoder for Anomaly Detection by openid_wwlPw9oj is a document available to read on EtoBox.

What is Explainable Autoencoder for Anomaly Detection about?

The document presents the Explainable Outlier Tree-based Encoder (EOTE), a novel anomaly detection model that integrates classification and regression trees within an autoencoder framework, aimed at improving explainability and handling mixed-attribute datasets. EOTE is evaluated against 12 leading algorithms across 110 datasets, demonstrating strong performance in detecting outliers while providing human-readable explanations, making it suitable for high-risk applications. The paper discusses the limitatio

Author

openid_wwlPw9oj

Language

EN