3 citations · 4 across the 5 of their papers we have counts for
5 papers
Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text
Phuong Nam Lê, Charlotte Schneider-Depré, Alexandre Goossens +3
Efficient planning, resource management, and consistent operations often rely on converting textual process documents into formal Business Process Model and Notation (BPMN) models.…
Crafting Imperceptible On-Manifold Adversarial Attacks for Tabular Data
Zhipeng He, Alexander Stevens, Chun Ouyang +3
Adversarial attacks on tabular data present unique challenges due to the heterogeneous nature of mixed categorical and numerical features. Unlike images where pixel perturbations m…
Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders
Alexander Stevens, Jari Peeperkorn, Johannes De Smedt +1
In predictive process monitoring, predictive models are vulnerable to adversarial attacks, where input perturbations can lead to incorrect predictions. Unlike in computer vision, w…
Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processes
Alexander Stevens, Chun Ouyang, Johannes De Smedt +1
In recent years, various machine and deep learning architectures have been successfully introduced to the field of predictive process analytics. Nevertheless, the inherent opacity…
Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models
Alexander Stevens, Johannes De Smedt
Although a recent shift has been made in the field of predictive process monitoring to use models from the explainable artificial intelligence field, the evaluation still occurs ma…