Research profile
We live in a hyper-connected world, where many systems are connecting to each other and influencing each other. This complex network of systems can produce large amounts of data. Both the network and the nature of the data it produces can evolve fast. Decisions need to be taken on a continuous basis in those complex settings addressing multiple criteria. People alone cannot deal with the amount and complexity of data, and machines alone are missing the required problem domain overview and context knowledge. Therefore, there is need for intelligent interaction between the human and the machine in data-driven decision making.
Decisions are in the centre of intelligent interaction. Those decisions are diffused into actions that constitute processes, which are executed in a business or societal context. The data generated by those processes can be stored in many information systems and are of a heterogenous nature. Data coming from multiple systems and other sources including IoT and social media, are transformed into information using data fusion and data science methods. This information combined with the tacit knowledge from the user is the basis for decisions. The context provides requirements for those decisions, i.e. the criteria to take into account in a decision. This closes the loop of the multi-criteria data-driven decision making in a complex system, which is the essence of Intelligent Interaction.
My research focus is on building three interrelated research lines:
- Supporting interaction between machine and human for joint decision making: There is a need for an intelligent interaction between human and machine supporting joint decision making, e.g., by allowing a machine to explain a decision but also incorporating people’s arguments and reasoning in the decision model. Hence this would enable having a ‘discussion’ with the machine for the best decision making.
- Information and data fusion: With the data stored in multiple sources, there is a need for techniques and methods integrating those heterogeneous data as well as analysing data with complex and non-standard structures.
- Decision contextualizing: In the Intelligent Interaction framework decisions are diffused and influence multiple systems, which can have different objectives. Therefore, in reaching the right decision those objectives should be considered, hence requiring a multi-criteria decision making. However, the challenge is that in many applications the data dimensions do not match the decision criteria.
Publications
Core publications:
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Moscicka, A., Pokonieczny, K., Wilbik, A., & Wabinski, J. (2019). Transport Accessibility of Warsaw: A Case Study. Sustainability, 11(19), [5536]. https://doi.org/10.3390/su11195536
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van Loon, S. L. M., Deneer, R., Nienhuijs, S. W., Wilbik, A., Kaymak, U., van Riel, N., Scharnhorst, V., & Boer, A-K. (2020). Metabolic Health Index (MHI): Assessment of Comorbidity in Bariatric Patients Based on Biomarkers. Obesity Surgery, 30(2), 714-724. https://doi.org/10.1007/s11695-019-04244-1
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van Loon, S. L. M., Wilbik, A. M., Kaymak, U., van den Heuvel, E. R., Scharnhorst, V., & Boer, A-K. (2018). Improved testing for vitamin B-12 deficiency: correcting MMA for eGFR reduces the number of patients classified as vitamin B-12 deficient. Annals of Clinical Biochemistry, 55(6), 685-692. https://doi.org/10.1177/0004563218778300
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Dijkman, R. M., & Wilbik, A. (2017). Linguistic summarization of event logs - A practical approach. Information Systems, 67, 114-125. https://doi.org/10.1016/J.IS.2017.03.009
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Alexander, G. L., Wilbik, A. M., Keller, J. M., & Musterman, K. (2014). Generating sensor data summaries to communicate change in elder’s health status. Applied Clinical Informatics, 5(1), 73-84. https://doi.org/10.4338/ACI-2013-07-RA-0050
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Wilbik, A., Keller, J. M., & Bezdek, J. C. (2014). Linguistic Prototypes for Data From Eldercare Residents. Ieee Transactions on Fuzzy Systems, 22(1), 110-123. https://doi.org/10.1109/TFUZZ.2013.2249517
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Wilbik, A., & Keller, J. M. (2013). A Fuzzy Measure Similarity Between Sets of Linguistic Summaries. Ieee Transactions on Fuzzy Systems, 21(1), 183-189. https://doi.org/10.1109/TFUZZ.2012.2214225
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Wilbik, A., Keller, J. M., & Alexander, G. L. (2012). Similarity evaluation of sets of linguistic summaries. International Journal of Intelligent Systems, 27(10), 926-938. https://doi.org/10.1002/INT.21555
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Wilbik, A., & Keller, J. M. (2012). A distance metric for a space of linguistic summaries. Fuzzy Sets and Systems, 208, 79-94. https://doi.org/10.1016/J.FSS.2012.03.010
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Kacprzyk, J., Wilbik, A., & Zadrozny, S. (2008). Linguistic summarization of time series using a fuzzy quantifier driven aggregation. Fuzzy Sets and Systems, 159(12), 1485-1499. https://doi.org/10.1016/J.FSS.2008.01.025
Most recent publications:
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Yurt, Z. O., Eshuis, R., Wilbik, A., & Vanderfeesten, I. (2022). Context-aware modeling for knowledge-intensive medicinal product development processes. Software and Systems Modeling. https://doi.org/10.1007/s10270-022-01070-5
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Khan, A., Thij, M. T., & Wilbik, A. (2022). Vertical Federated Learning: A Structured Literature Review.
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Wrede, C., Winands, M. H. M., & Wilbik, A. (2022). Linguistic Summaries as Explanation Mechanism for Classification Problems. Paper presented at The 34rd Benelux Conference on Artificial Intelligence and the 31th Belgian Dutch Conference on Machine Learning, Mechelen, Belgium. https://bnaic2022.uantwerpen.be/wp-content/uploads/BNAICBeNeLearn_2022_submission_6085.pdf
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Stallmann, M., & Wilbik, A. (2022). On a Framework for Federated Cluster Analysis. Applied Sciences, 12(20), [10455]. https://doi.org/10.3390/app122010455
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Khan, A., ten Thij, M., & Wilbik, A. (2022). Communication-Efficient Vertical Federated Learning. Algorithms, 15(8), [273]. https://doi.org/10.3390/a15080273
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Yurt, Z. O., Eshuis, R., Aysolmaz, B., Wilbik, A., & Vanderfeesten, I. T. P. (2022). A Method for Integrated Modeling of KiPs and Contextual Goals. In M. Sellami, P. Ceravolo, H. A. Reijers, W. Gaaloul, & H. Panetto (Eds.), Cooperative Information Systems: 28th International Conference, CoopIS 2022, Bozen-Bolzano, Italy, October 4–7, 2022, Proceedings (pp. 322-332). Springer, Cham. Lecture Notes in Computer Science Vol. 13591 https://doi.org/10.1007/978-3-031-17834-4_20
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Aechtner, J., Cabrera, L., Katwal, D., Onghena, P., Valenzuela, D. P., & Wilbik, A. (2022). Comparing User Perception of Explanations Developed with XAI Methods. In 2022 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) IEEE. IEEE International Fuzzy Systems Conference Proceedings https://doi.org/10.1109/FUZZ-IEEE55066.2022.9882743
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Chouhan, S., Wilbik, A., & Dijkman, R. (2022). Explanation of Anomalies in Business Process Event Logs with Linguistic Summaries. In 2022 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) IEEE. IEEE International Fuzzy Systems Conference Proceedings https://doi.org/10.1109/FUZZ-IEEE55066.2022.9882673
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Wilbik, A. (2022). Federated Learning: A Hype or a Trend?. 9-11. Paper presented at 14th International joint conference on computational intelligence, Valetta, Malta. https://doi.org/10.5220/0011598500003332
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Dyczkowski, K., Pckala, B., Szkola, J., & Wilbik, A. (2022). Federated learning with uncertainty on the example of a medical data. In 2022 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) - Proceedings IEEE. IEEE International Fuzzy Systems Conference Proceedings https://doi.org/10.1109/FUZZ-IEEE55066.2022.9882862
Other publications:

A .M. Wilbik
Professor in Data Fusion and Intelligent Interaction