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Interpretable Artificial Intelligence: A Perspective of Granular Computing

  • Book
  • © 2021

Overview

Editors:
  1. Witold Pedrycz

    1. Department of Electrical and Computer Engineering Edmonton, University of Alberta, Edmonton, Canada

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    You can also search for this editor in PubMed   Google Scholar

  2. Shyi-Ming Chen

    1. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Highlights recent research on interpretable Artificial Intelligence
  • Focuses on a perspective of Granular Computing
  • Written by experts in the field

Part of the book series: Studies in Computational Intelligence (SCI, volume 937)

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-viii
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  2. Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring

    • Nijat Mehdiyev, Peter Fettke
    Pages 1-28

  3. Use of Visual Analytics (VA) in Explainable Artificial Intelligence (XAI): A Framework of Information Granules

    • Bo Sun
    Pages 29-62

  4. Visualizing the Behavior of Convolutional Neural Networks for Time Series Forecasting

    • Janosch Henze, Bernhard Sick
    Pages 63-89

  5. Beyond Deep Event Prediction: Deep Event Understanding Based on Explainable Artificial Intelligence

    • Bukhoree Sahoh, Anant Choksuriwong
    Pages 91-117

  6. Interpretation of SVM to Build an Explainable AI via Granular Computing

    • Sanjay Sekar Samuel, Nik Nailah Binti Abdullah, Anil Raj
    Pages 119-152

  7. Factual and Counterfactual Explanation of Fuzzy Information Granules

    • Ilia Stepin, Alejandro Catala, Martin Pereira-Fariña, Jose M. Alonso
    Pages 153-185

  8. Transparency and Granularity in the SP Theory of Intelligence and Its Realisation in the SP Computer Model

    • J. Gerard Wolff
    Pages 187-216

  9. Survey of Explainable Machine Learning with Visual and Granular Methods Beyond Quasi-Explanations

    • Boris Kovalerchuk, Muhammad Aurangzeb Ahmad, Ankur Teredesai
    Pages 217-267

  10. MiBeX: Malware-Inserted Benign Datasets for Explainable Machine Learning

    • Wayne Stegner, Tyler Westland, David Kapp, Temesguen Kebede, Rashmi Jha
    Pages 269-291

  11. Designing Explainable Text Classification Pipelines: Insights from IT Ticket Complexity Prediction Case Study

    • Aleksandra Revina, Krisztian Buza, Vera G. Meister
    Pages 293-332

  12. A Granular Computing Approach to Provide Transparency of Intelligent Systems for Criminal Investigations

    • Sam Hepenstal, Leishi Zhang, Neesha Kodagoda, B. L. William Wong
    Pages 333-367

  13. RYEL System: A Novel Method for Capturing and Represent Knowledge in a Legal Domain Using Explainable Artificial Intelligence (XAI) and Granular Computing (GrC)

    • Luis Raúl Rodríguez Oconitrillo, Juan José Vargas, Arturo Camacho, Alvaro Burgos, Juan Manuel Corchado
    Pages 369-399

  14. A Generative Model Based Approach for Zero-Shot Breast Cancer Segmentation Explaining Pixels’ Contribution to the Model’s Prediction

    • Preeti Mukherjee, Mainak Pal, Lidia Ghosh, Amit Konar
    Pages 401-425

  15. Back Matter

    Pages 427-429
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Keywords

About this book

This book offers a comprehensive treatise on the recent pursuits of Artificial Intelligence (AI) – Explainable Artificial Intelligence (XAI) by casting the crucial features of interpretability and explainability in the original framework of Granular Computing. The innovative perspective established with the aid of information granules provides a high level of human centricity and transparency central to the development of AI constructs. The chapters reflect the breadth of the area and cover recent developments in the methodology, advanced algorithms and applications of XAI to visual analytics, knowledge representation, learning and interpretation. The book appeals to a broad audience including researchers and practitioners interested in gaining exposure to the rapidly growing body of knowledge in AI and intelligent systems.

Editors and Affiliations

  • Department of Electrical and Computer Engineering Edmonton, University of Alberta, Edmonton, Canada

    Witold Pedrycz

  • Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

Bibliographic Information

  • Book Title: Interpretable Artificial Intelligence: A Perspective of Granular Computing

  • Editors: Witold Pedrycz, Shyi-Ming Chen

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-030-64949-4

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

  • Hardcover ISBN: 978-3-030-64948-7Published: 27 March 2021

  • Softcover ISBN: 978-3-030-64951-7Published: 29 March 2022

  • eBook ISBN: 978-3-030-64949-4Published: 26 March 2021

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: VIII, 429

  • Number of Illustrations: 32 b/w illustrations, 138 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence

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