Explainable AI
Understanding models by explainable AI techniques helps to effectively build models tailored to the specific challenges of the various application areas.
Publications
2023[ to top ]
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Feature relevance XAI in anomaly detection: Reviewing approaches and challenges. . In Frontiers in Artificial Intelligence, 6. 2023.
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Occupational Fraud Detection through Agent-based Data Generation. . In The 8th Workshop on MIning DAta for financial applicationS MIDAS 2023 - to appear. 2023.
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Evaluating feature relevance XAI in network intrusion detection. . In The World Conference on eXplainable Artificial Intelligence (xAI 2023) - to appear. 2023.
2022[ to top ]
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Open ERP System Data For Occupational Fraud Detection. . In arxiv. 2022.
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Towards Explainable Occupational Fraud Detection. . In Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022, Communications in Computer and Information Science(1753), pp. 79–96. 2022.
2021[ to top ]
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A financial game with opportunities for fraud. . In IEE COG 2021, 2021. 2021.
2020[ to top ]
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Evaluation of post-hoc XAI approaches through synthetic tabular data. . In International Symposium on Methodologies for Intelligent Systems. Springer, 2020.
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Evaluation of Post-hoc XAI Approaches Through Synthetic Tabular Data. . In Foundations of Intelligent Systems, D. Helic, G. Leitner, M. Stettinger, A. Felfernig, Z. W. Ra’s}} (eds.), pp. 422–430. Springer International Publishing, Cham, 2020.