Abstract
Explanation, or system interpretability, has always been important in applications where critical decisions need to be made, for example in the justice system or biomedical applications. In artificial intelligence and machine learning, there is an ever increasing need for system interpretability. This paper investigates a Fuzzy Multi-Criteria Decision-Making (MCDM) model as the basis for an interpretable framework for explainable classification. The proposed framework includes a Fuzzy Inference System paired with a modified MCDM-based model for data-driven classification. The modular nature of MCDM allows for the development of a model-based layer capable of generating factual and counterfactual explanations. Results on a ‘Titanic’ survivors’ dataset classification, which illustrates a minimal trade-off in predictive performance while gaining textual and graphical explanation, autonomously provided by the proposed model-based MCDM framework.
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References
Molnar, C.: Interpretable Machine Learning. Lulu Press Inc., Morrisville (2020). https://christophm.github.io/interpretable-ml-book
Ribeiro, M.T., Singh, S., Guestrin, C.: Model-agnostic interpretability of machine learning. Technical Report (2016)
Lipton, Z.C.: The mythos of model interpretability. in machine learning, the concept of interpretability is both important and slippery. Queue 16(3), 31–57 (2018)
Rudin, C., Radin, J.: Why are we using black box models in AI when we don’t need to? A lesson from an explainable AI competition. Harvard Data Sci. Rev. 1(2) (2019)
Mencar, C., Alonso, J.M.: Paving the way to explainable artificial intelligence with fuzzy modeling. In: Fullér, R., Giove, S., Masulli, F. (eds.) WILF 2018. LNCS (LNAI), vol. 11291, pp. 215–227. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12544-8_17
Kaggle.com. Titanic - Machine Learning from Disaster—Kaggle (2021). https://www.kaggle.com/c/titanic. Accessed 28 May 2021
Pohekar, S.D., Ramachandran, M.: Application of multi-criteria decision making to sustainable energy planning - a review. Renew. Sustain. Energy Rev. 8(4), 365–381 (2004)
Mardani, A., Jusoh, A., Nor, K., Khalifah, Z., Zakuan, N., Valipour, A.: Multiple criteria decision making techniques and its applications - a review of the literature from 2000 to 2014. Econ. Res. 28(1), 516–517 (2015)
Baccour, L.: Amended fused TOPSIS-VIKOR for classification (ATOVIC) applied to some UCI data sets. Expert Syst. Appl. 99, 115–125 (2018)
Piegat, A., Sałabun, W.: Comparative analysis of MCDM methods for assessing the severity of chronic liver disease. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS (LNAI), vol. 9119, pp. 228–238. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19324-3_21
Yusuf, H., Panoutsos, G.: Multi-criteria decision making using Fuzzy Logic and ATOVIC with application to manufacturing. In: 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–7 (2020)
Moradi, M., Samwald, M.: Post-hoc explanation of black-box classifiers using confident itemsets. Expert Syst. Appl. 165, 113941 (2021)
Gill, N., Hall, P., Montgomery, K., Schmidt, N.: A responsible machine learning workflow with focus on interpretable models, post-hoc explanation, and discrimination testing. Information 11(3), 1–32 (2020)
Stepin, I., Alonso, J.M., Catala, A., Pereira-Fariña, M.: Generation and evaluation of factual and counterfactual explanations for decision trees and fuzzy rule-based classifiers. In: 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8 (2020)
Asuero, A.G., Sayago, A., González, A.G.: The correlation coefficient: an overview. Crit. Rev. Anal. Chem. 36(1), 41–59 (2006)
Ekinci, E., Acun, N., Omurca, S.İ.: A comparative study on machine learning techniques using titanic dataset. In: 7th International Conference on Advanced Technologies - ICAT 2018, pp. 411–416 (2018)
Acknowledgement
This publication was made possible by the sponsorship and support of Lloyd’s Register Foundation. A charitable foundation, helping to protect life and property by supporting engineering-related education, public engagement and the application of research www.lrfoundation.org.uk. The work was enabled through, and undertaken at, the National Structural Integrity Research Centre (NSIRC), a postgraduate engineering facility for industry-led research into structural integrity established and managed by TWI through a network of both national and international Universities. This research was also financially supported by The University of Sheffield, Department of Automatic Control and Systems Engineering.
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Yusuf, H., Yang, K., Panoutsos, G. (2022). Fuzzy Multi-Criteria Decision-Making: Example of an Explainable Classification Framework. In: Jansen, T., Jensen, R., Mac Parthaláin, N., Lin, CM. (eds) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing, vol 1409. Springer, Cham. https://doi.org/10.1007/978-3-030-87094-2_2
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