© 2019

Uncertainty Modelling in Data Science

  • Sébastien Destercke
  • Thierry Denoeux
  • María Ángeles Gil
  • Przemyslaw Grzegorzewski
  • Olgierd Hryniewicz
Conference proceedings SMPS 2018

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 832)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Irene Arellano, Beatriz Sinova, Sara de la Rosa de Sáa, María Asunción Lubiano, María Ángeles Gil
    Pages 9-16
  3. Michal Burda, Petra Murinová, Viktor Pavliska
    Pages 25-32
  4. Giulianella Coletti, Davide Petturiti, Barbara Vantaggi
    Pages 33-41
  5. Bernard De Baets, Hans De Meyer
    Pages 42-45
  6. Jasper De Bock, Gert de Cooman
    Pages 46-53
  7. Guillaume Dendievel, Sebastien Destercke, Pierre Wachalski
    Pages 59-67
  8. Didier Dubois, Henri Prade
    Pages 68-77
  9. Maria Brigida Ferraro, Paolo Giordani
    Pages 87-90
  10. Przemyslaw Grzegorzewski
    Pages 91-98
  11. Olgierd Hryniewicz, Katarzyna Kaczmarek-Majer
    Pages 107-114
  12. Katarzyna Kaczmarek-Majer, Olgierd Hryniewicz, Karol R. Opara, Weronika Radziszewska, Anna Olwert, Jan W. Owsiński et al.
    Pages 115-123
  13. Daniel Krpelik, Frank P. A. Coolen, Louis J. M. Aslett
    Pages 133-140
  14. Jiří Kupka, Pavel Rusnok
    Pages 141-148

About these proceedings


This book features 29 peer-reviewed papers presented at the 9th International Conference on Soft Methods in Probability and Statistics (SMPS 2018), which was held in conjunction with the 5th International Conference on Belief Functions (BELIEF 2018) in Compiègne, France on September 17–21, 2018. It includes foundational, methodological and applied contributions on topics as varied as imprecise data handling, linguistic summaries, model coherence, imprecise Markov chains, and robust optimisation. These proceedings were produced using EasyChair. 

Over recent decades, interest in extensions and alternatives to probability and statistics has increased significantly in diverse areas, including decision-making, data mining and machine learning, and optimisation. This interest stems from the need to enrich existing models, in order to include different facets of uncertainty, like ignorance, vagueness, randomness, conflict or imprecision. Frameworks such as rough sets, fuzzy sets, fuzzy random variables, random sets, belief functions, possibility theory, imprecise probabilities, lower previsions, and desirable gambles all share this goal, but have emerged from different needs. 

The advances, results and tools presented in this book are important in the ubiquitous and fast-growing fields of data science, machine learning and artificial intelligence. Indeed, an important aspect of some of the learned predictive models is the trust placed in them. 

Modelling the uncertainty associated with the data and the models carefully and with principled methods is one of the means of increasing this trust, as the model will then be able to distinguish between reliable and less reliable predictions. In addition, extensions such as fuzzy sets can be explicitly designed to provide interpretable predictive models, facilitating user interaction and increasing trust.


Computational Intelligence Intelligent Data Analysis Soft Computing SMPS 2018 Statistics

Editors and affiliations

  • Sébastien Destercke
    • 1
  • Thierry Denoeux
    • 2
  • María Ángeles Gil
    • 3
  • Przemyslaw Grzegorzewski
    • 4
  • Olgierd Hryniewicz
    • 5
  1. 1.CNRS, HeudiasycSorbonne universités, Université de technologie de CompiègneCompiegneFrance
  2. 2.CNRS, HeudiasycSorbonne universités, Université de technologie de CompiègneCompiegneFrance
  3. 3.Department of Statistics and Operational Research and Mathematics DidacticsUniversity of OviedoOviedoSpain
  4. 4.Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarsawPoland
  5. 5.Department of Stochastic Methods, Systems Research InstitutePolish Academy of SciencesWarsawPoland

About the editors

Bibliographic information

  • Book Title Uncertainty Modelling in Data Science
  • Editors Sébastien Destercke
    Thierry Denoeux
    María Ángeles Gil
    Przemyslaw Grzegorzewski
    Olgierd Hryniewicz
  • Series Title Advances in Intelligent Systems and Computing
  • Series Abbreviated Title Advs in Intelligent Syst., Computing
  • DOI
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Intelligent Technologies and Robotics Intelligent Technologies and Robotics (R0)
  • Softcover ISBN 978-3-319-97546-7
  • eBook ISBN 978-3-319-97547-4
  • Series ISSN 2194-5357
  • Series E-ISSN 2194-5365
  • Edition Number 1
  • Number of Pages XI, 234
  • Number of Illustrations 22 b/w illustrations, 0 illustrations in colour
  • Topics Computational Intelligence
    Artificial Intelligence
  • Buy this book on publisher's site
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