1. Ademujimi, T., & Prabhu, V. (2021). Fusion-Learning of Bayesian Network Models for Fault Diagnostics. Sensors, 21(22), 7633. DOI: 10.3390/s21227633
2. Afrabandpey, H., Peltola, T., & Kaski, S. (2019, 8/2019). Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets. Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19},
3. Alkofahi, H., Umphress, D., & Alawneh, H. (2022, 2022). Discovering Conditional Business Rules in Web Applications Using Process Mining.Lecture Notes in Computer Science
4. Basden, A. (2011). A presentation of Herman Dooyeweerd's aspects of temporal reality. International Journal of Multi-aspectual Practice, 1(1), 1-40. http://usir.salford.ac.uk/id/eprint/31424/
5. Ben Brahim, I., Addouche, S.-A., El Mhamedi, A., & Boujelbene, Y. (2022). Cluster-based WSA method to elicit expert knowledge for Bayesian reasoning—Case of parcel delivery with drone. Expert Systems with Applications, 191, 116160. DOI: 10.1016/j.eswa.2021.116160
6. Campos, J., Richetti, P., Baião, F. A., & Santoro, F. M. (2018). Discovering Business Rules in Knowledge-Intensive Processes Through Decision Mining: An Experimental Study. In E. Teniente & M. Weidlich, Business Process Management Workshops Cham.
7. Cheung, C. F., Lee, W. B., Wang, W. M., Wang, Y., & Yeung, W. M. (2011). A multi-faceted and automatic knowledge elicitation system (MAKES) for managing unstructured information. Expert Systems with Applications, 38(5), 5245-5258. DOI: 10.1016/j.eswa.2010.10.033
8. Crerie, R., Baião, F., & Santoro, F. (2009). Discovering Business Rules through Process Mining (Vol. 29).
9. D’Angelo, G., & Palmieri, F. (2020). Knowledge elicitation based on genetic programming for non destructive testing of critical aerospace systems. Future Generation Computer Systems, 102, 633-642. DOI: 10.1016/j.future.2019.09.007
10. El-Assady, M., Kehlbeck, R., Collins, C., Keim, D., & Deussen, O. (2020). Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections. IEEE Transactions on Visualization and Computer Graphics, 26(1), 1001-1011. DOI: 10.1109/TVCG.2019.2934654
11. El-Assady, M., Sperrle, F., Deussen, O., Keim, D., & Collins, C. (2019). Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution. IEEE Transactions on Visualization and Computer Graphics, 25(1), 374-384. DOI: 10.1109/TVCG.2018.2864769
12. Harvey, A. C. (1990). ARIMA Models. In J. Eatwell, M. Milgate, & P. Newman (Eds.), Time Series and Statistics (pp. 22-24). Palgrave Macmillan UK. DOI: 10.1007/978-1-349-20865-4_2
13. Hu, R. L., Granderson, J., Auslander, D. M., & Agogino, A. (2019). Design of machine learning models with domain experts for automated sensor selection for energy fault detection. Applied Energy, 235, 117-128. DOI: 10.1016/j.apenergy.2018.10.107
14. Huang, L., Cai, G., Yuan, H., & Chen, J. (2019). A hybrid approach for identifying the structure of a Bayesian network model. Expert Systems with Applications, 131, 308-320. DOI: 10.1016/j.eswa.2019.04.060
15. Lee, M. H., Siewiorek, D. P., Smailagic, A., Bernardino, A., & Bermúdez i Badia, S. (2020, April 2, 2020). Interactive hybrid approach to combine machine and human intelligence for personalized rehabilitation assessment.CHIL '20
16. Lim, B., Arik, S. O., Loeff, N., & Pfister, T. (2020). Temporal Fusion Transformers for Interpretable Multi-horizon Time Series
17. Forecasting. DOI: 10.48550/arXiv.1912.09363
18. Mantik, S., Li, M., & Porteous, J. (2022). A preference elicitation framework for automated planning. Expert Systems with Applications, 208, 118014. DOI: 10.1016/j.eswa.2022.118014
19. Možina, M., Lazar, T., & Bratko, I. (2018). Identifying typical approaches and errors in Prolog programming with argument-based machine learning. Expert Systems with Applications, 112, 110-124. DOI: 10.1016/j.eswa.2018.06.029
20. The Organic Products Regulations 2009. https://www.legislation.gov.uk/uksi/2009/842/made/data.xht?view=snippet&wrap=true
21. Park, H., Megahed, A., Yin, P., Ong, Y., Mahajan, P., & Guo, P. (2023). Incorporating experts’ judgment into machine learning models. Expert Systems with Applications, 120118. DOI: 10.1016/j.eswa.2023.120118
22. Park, S., Wang, A., Kawas, B., Liao, Q. V., Piorkowski, D., & Danilevsky, M. (2021). Facilitating Knowledge Sharing from Domain Experts to Data Scientists
23. for Building NLP Models. DOI: 10.48550/arXiv.2102.00036
24. Seymoens, T., Ongenae, F., Jacobs, A., Verstichel, S., & Ackaert, A. (2019, 2019). A Methodology to Involve Domain Experts and Machine Learning Techniques in the Design of Human-Centered Algorithms.IFIP Advances in Information and Communication Technology
25. Sundin, I., Voronov, A., Xiao, H., Papadopoulos, K., Bjerrum, E. J., Heinonen, M., Patronov, A., Kaski, S., & Engkvist, O. (2022). Human-in-the-loop assisted de novo molecular design. Journal of Cheminformatics, 14(1). DOI: 10.1186/s13321-022-00667-8
26. Wang, D., Andres, J., Weisz, J., Oduor, E., & Dugan, C. (2021, 2021-05-06). AutoDS: Towards Human-Centered Automation of Data Science.
27. Wen, R., Torkkola, K., Narayanaswamy, B., & Madeka, D. (2018). A Multi-Horizon Quantile Recurrent Forecaster. DOI: 10.48550/arXiv.1711.11053
28. Winfield, M. J. (2000). Multi-aspectual knowledge elicitation [phd, Salford : University of Salford]. usir.salford.ac.uk. https://usir.salford.ac.uk/id/eprint/26965/
29. Yazici, I., Beyca, O. F., Gurcan, O. F., Zaim, H., Delen, D., & Zaim, S. (2022). A comparative analysis of machine learning techniques and fuzzy analytic hierarchy process to determine the tacit knowledge criteria. Annals of Operations Research, 308(1/2), 753-776. DOI: 10.1007/s10479-020-03697-3
30. Young, A., West, G., Brown, B., Stephen, B., Duncan, A., Michie, C., & Mcarthur, S. D. J. (2022). Parameterisation of domain knowledge for rapid and iterative prototyping of knowledge-based systems. Expert Systems with Applications, 208, 118169. DOI: 10.1016/j.eswa.2022.118169

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