Deep reinforcement learning for data-efficient weakly supervised business process anomaly detection

Deep reinforcement learning for data-efficient weakly supervised business process anomaly detection

Abstract The detection of anomalous behavior in business process data is a crucial task for preventing failures that may jeopardize the performance of any organization. Supervised learning techniques are impracticable because of the difficulties of gathering huge amounts of labeled business process...

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Bibliographic Details
Main Authors: Eman Abd Elaziz, Radwa Fathalla, Mohamed Shaheen
Format: Article
Language:English
Published: SpringerOpen 2023-03-01
Series:Journal of Big Data
Subjects:
Business process
Deep reinforcement learning
Weakly supervised learning
Variational autoencoder
Long short-term memory
Self-attention
Online Access:https://doi.org/10.1186/s40537-023-00708-5

Internet

https://doi.org/10.1186/s40537-023-00708-5

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