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2020 – today
- 2024
[j2]Alexander Stevens
, Johannes De Smedt:
Explainability in process outcome prediction: Guidelines to obtain interpretable and faithful models. Eur. J. Oper. Res. 317(2): 317-329 (2024)- [j1]Manon Reusens, Alexander Stevens, Jonathan Tonglet, Johannes De Smedt, Wouter Verbeke, Seppe vanden Broucke, Bart Baesens:
Evaluating text classification: A benchmark study. Expert Syst. Appl. 254: 124302 (2024) - [i1]Alexander Stevens, Chun Ouyang, Johannes De Smedt, Catarina Moreira:
Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processes. CoRR abs/2403.09232 (2024) - 2023
- [c5]Alexander Stevens, Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt:
Manifold Learning for Adversarial Robustness in Predictive Process Monitoring. ICPM 2023: 17-24 - 2022
- [c4]Alexander Stevens, Johannes De Smedt, Jari Peeperkorn, Jochen De Weerdt:
Assessing the Robustness in Predictive Process Monitoring through Adversarial Attacks. ICPM 2022: 56-63 - [c3]Jari Peeperkorn, Carlos Ortega Vázquez, Alexander Stevens, Johannes De Smedt, Seppe vanden Broucke, Jochen De Weerdt:
Outcome-Oriented Predictive Process Monitoring on Positive and Unlabelled Event Logs. ICPM Workshops 2022: 255-268 - 2021
- [c2]Alexander Stevens, Johannes De Smedt, Jari Peeperkorn:
Quantifying Explainability in Outcome-Oriented Predictive Process Monitoring. ICPM Workshops 2021: 194-206 - 2020
- [c1]Alexander Stevens, Peter Deruyck, Ziboud Van Veldhoven, Jan Vanthienen:
Explainability and Fairness in Machine Learning: Improve Fair End-to-end Lending for Kiva. SSCI 2020: 1241-1248
Coauthor Index
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