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DARWIN: An online deep learning approach to handle concept drifts in predictive process monitoring

  • Published:2023-08 Issue: Volume:123 Page:106461
  • ISSN:0952-1976
  • Container-title:Engineering Applications of Artificial Intelligence
  • language:en
  • Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Pasquadibisceglie VincenzoORCID,Appice Annalisa,Castellano GiovannaORCID,Malerba DonatoORCID

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference72 articles.

1. Process mining manifesto;Aalst,2011

2. Concept drift detection in data stream mining : A literature review;Agrahari;J. King Saud Univ. - Comput. Inf. Sci.,2021

3. SENECA: change detection in optical imagery using siamese networks with active-transfer learning;Andresini;Expert Syst. Appl.,2023

4. EUPHORIA: A neural multi-view approach to combine content and behavioral features in review spam detection;Andresini;J. Comput. Math. Data Sci.,2022

5. INSOMNIA: towards concept-drift robustness in network intrusion detection;Andresini,2021

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Thwarting Cybersecurity Attacks with Explainable Concept Drift;2024 International Wireless Communications and Mobile Computing (IWCMC);2024-05-27

2. Tuning Machine Learning to Address Process Mining Requirements;IEEE Access;2024

3. A Stream Data Mining Approach to Handle Concept Drifts in Process Discovery;Lecture Notes in Computer Science;2024

4. TSUNAMI - an explainable PPM approach for customer churn prediction in evolving retail data environments;Journal of Intelligent Information Systems;2023-12-28

5. Distributed Concept Drift Detection for Efficient Model Adaptation with Big Data Streams;2023 IEEE International Conference on Big Data (BigData);2023-12-15
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