{"total":19,"rows":[{"id":"2210.06959","title":"A Survey on Explainable Anomaly Detection","created":"2022-10-13","cited_by_count":39,"area":null},{"id":"2112.08442","title":"Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)","created":"2021-12-14","cited_by_count":15,"area":null},{"id":"2308.00074","title":"Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model","created":"2023-07-31","cited_by_count":10,"area":null},{"id":"2105.06314","title":"Explainable Machine Learning for Fraud Detection","created":"2021-05-13","cited_by_count":9,"area":null},{"id":"2103.03302","title":"Ensembles of Random SHAPs","created":"2021-03-04","cited_by_count":8,"area":null},{"id":"2302.06670","title":"Unveiling the Unseen: A Comprehensive Survey on Explainable Anomaly Detection in Images and Videos","created":"2023-02-13","cited_by_count":7,"area":null},{"id":"2106.09111","title":"An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data","created":"2021-06-16","cited_by_count":6,"area":null},{"id":"2502.10211","title":"Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction","created":"2025-02-14","cited_by_count":6,"area":null},{"id":"2209.10658","title":"Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data","created":"2022-09-21","cited_by_count":4,"area":null},{"id":"2209.09157","title":"RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations","created":"2022-09-19","cited_by_count":2,"area":null},{"id":"2303.08625","title":"Interpretable Ensembles of Hyper-Rectangles as Base Models","created":"2023-03-15","cited_by_count":2,"area":null},{"id":"2403.01245","title":"AcME-AD: Accelerated Model Explanations for Anomaly Detection","created":"2024-03-02","cited_by_count":2,"area":null},{"id":"2404.18525","title":"Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD","created":"2024-04-29","cited_by_count":2,"area":null},{"id":"2308.12612","title":"Try with Simpler -- An Evaluation of Improved Principal Component Analysis in Log-based Anomaly Detection","created":"2023-08-24","cited_by_count":1,"area":null},{"id":"2507.21023","title":"On Using the Shapley Value for Anomaly Localization: A Statistical Investigation","created":"2025-07-28","cited_by_count":1,"area":null},{"id":"2602.21766","title":"RAMSeS: Robust and Adaptive Model Selection for Time-Series Anomaly Detection Algorithms","created":"2026-02-25","cited_by_count":1,"area":null},{"id":"2606.09874","title":"Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detection","created":"2026-06-02","cited_by_count":1,"area":null},{"id":"2606.00867","title":"Statistical Analysis of using the Shapley Value for Sensor Anomaly Localization with Accurate Classifiers","created":"2026-05-30","cited_by_count":0,"area":null},{"id":"2609.03826","title":"Witnesses Explain Anomalies","created":"2026-09-03","cited_by_count":0,"area":null}],"next_cursor":null}