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4 4.8. References 91 - Developing a benchmark for fair comparison of algorithms for automatic Fault Tree model inference to understand their comparative advantages and drawbacks, ensuring uniform and thorough evaluation. - Extending our approach to handle missing information and noise, is crucial for realistic scenarios where data may be incomplete or inaccurate. 4.8 References Abdi, H. and L. J. Williams (2010). “Principal component analysis”. In: WIREs Computational Statistics 2.4, pp. 433–459. doi: 10.1002/wics.101. Bakeli, T., A. A. Hafidi, et al. (2020). “COVID-19 infection risk management during construction activities: An approach based on Fault Tree Analysis (FTA)”. In: Journal of Emergency Management 18.7, pp. 161–176. doi: 10.5055/jem.0539. Bo)i#, D., B. Runje, D. Lisjak, and D. Kolar (2023). “Metrics Related to Confusion Matrix as Tools for Conformity Assessment Decisions”. In: Applied Sciences 13.14. issn: 2076-3417. doi: 10.3390/app13148187. Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan (2002). “A fast and elitist multiobjective genetic algorithm: NSGA-II”. In: IEEE Transactions on Evolutionary Computation 6 (2), pp. 182–197. doi: 10.1109/4235.996017. Jimenez-Roa, L. A., N. Rusnac, M. Volk, and M. Stoelinga (2024). “Fault Tree Inference Using Multi-objective Evolutionary Algorithms and Confusion Matrix-Based Metrics”. In: Formal Methods for Industrial Critical Systems. Ed. by A. E. Haxthausen and W. Serwe. Cham: Springer Nature Switzerland, pp. 80–96. isbn: 978-3-031-68150-9. doi: 10.1007/978-3-031-68150-9_5. Jimenez-Roa, L. A., T. Heskes, T. Tinga, and M. Stoelinga (2023). “Automatic Inference of Fault Tree Models Via Multi-Objective Evolutionary Algorithms”. In: IEEE Transactions on Dependable and Secure Computing 20.4, pp. 3317–3327. doi: 10.1109/TDSC. 2022.3203805. Jimenez-Roa, L. A., M. Volk, and M. Stoelinga (2022). “Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization”. In: Computer Safety, Reliability, and Security. Ed. by M. Trapp, F. Saglietti, M. Spisländer, and F. Bitsch. Cham: Springer International Publishing, pp. 46–61. doi: 10.1007/978-3-031-14835-4_4. Lazarova-Molnar, S., P. Niloofar, and G. K. Barta (2020). “Data-Driven Fault Tree Modeling for Reliability Assessment of Cyber-Physical Systems”. In: 2020 Winter Simulation Conference (WSC), pp. 2719–2730. doi: 10.1109/WSC48552.2020.9383882. Linard, A., D. Bucur, and M. Stoelinga (2019). “Fault Trees from Data: E’cient Learning with an Evolutionary Algorithm”. In: International Symposium on Dependable Software Engineering: Theories, Tools, and Applications. Vol. 11951 LNCS, pp. 19–37. doi: 10.1007/978-3-030-35540-1_2. Madden, M. G. and P. J. Nolan (1994). “Generation of fault trees from simulated incipient fault case data”. In: WIT Transactions on Information and Communication Technologies 6. doi: 10.2495/AI940611. Martí, L., E. Segredo, N. (nchez-Pi, and E. Hart (2017). “Impact of selection methods on the diversity of many-objective Pareto set approximations”. In: Procedia Computer Science 112. Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 21st International Conference, KES-20176-8 September 2017, Marseille, France, pp. 844–853. issn: 1877-0509. doi: https://doi.org/10.1016/j.procs.2017. 08.077.

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