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Moving Towards Explainable Artificial Intelligence Using Fuzzy Rule-Based Networks in Decision-Making Process

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Advances in Information Systems, Artificial Intelligence and Knowledge Management (ICIKS 2023)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 486))

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Abstract

Advanced Machine Learning and Artificial Intelligence techniques are very powerful in predictive tasks and they are getting more popular as decision making tools across many industries and fields. However, they are mostly weak in explaining the inference and internal process and they are referred to as black-box models. Fuzzy Rule Based Network is a powerful white-box technique which maps well the external inputs, intermediate latent variables and outputs a modular approach based on Fuzzy Logic and it is capable of dealing with complexity and linguistic uncertainty in decision making process. To improve the performance of Fuzzy Rule Based Network, it requires to be tuned and optimized to increase its accuracy, transparency and efficiency. In this paper, a method is proposed to tune the Fuzzy Rule Based Network by using Fuzzy C-Mean and Genetic Algorithm for rule reduction and tuning membership functions and also Backward Selection techniques for pruning and input and branch selection. A case study in transport and telecommuting is used to illustrate the performance of the proposed method. The results show the Fuzzy Rule Based Network’s ability to explain the internal process of decision making and its capabilities in transparency, interpretability and in moving towards Explainable Artificial Intelligence (XAI).

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Author information

Authors and Affiliations

  1. School of Computing, University of Portsmouth, Portsmouth, UK

    Farzad Arabikhan, Alexander Gegov, Rahim Taheri & Mohamed Bader-EI-Den

  2. Portsmouth Business School, University of Portsmouth, Portsmouth, UK

    Negar Akbari

Authors
  1. Farzad Arabikhan
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  2. Alexander Gegov
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  3. Rahim Taheri
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  4. Negar Akbari
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  5. Mohamed Bader-EI-Den
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Corresponding author

Correspondence to Farzad Arabikhan .

Editor information

Editors and Affiliations

  1. ESC Amiens and University of Picardie Jules Verne, Amiens, France

    Inès Saad

  2. University of Paris Dauphine-PSL, Paris, France

    Camille Rosenthal-Sabroux

  3. University of Sfax, Sfax, Tunisia

    Faiez Gargouri

  4. University of Portsmouth, Portsmouth, UK

    Salem Chakhar

  5. University of Portsmouth, Portsmouth, UK

    Nigel Williams

  6. University of Portsmouth, Portsmouth, UK

    Ella Haig

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Arabikhan, F., Gegov, A., Taheri, R., Akbari, N., Bader-EI-Den, M. (2024). Moving Towards Explainable Artificial Intelligence Using Fuzzy Rule-Based Networks in Decision-Making Process. In: Saad, I., Rosenthal-Sabroux, C., Gargouri, F., Chakhar, S., Williams, N., Haig, E. (eds) Advances in Information Systems, Artificial Intelligence and Knowledge Management. ICIKS 2023. Lecture Notes in Business Information Processing, vol 486. Springer, Cham. https://doi.org/10.1007/978-3-031-51664-1_21

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  • DOI: https://doi.org/10.1007/978-3-031-51664-1_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-51663-4

  • Online ISBN: 978-3-031-51664-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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