Editors:
- Presents the latest research on data analysis and soft computing
- Includes outcomes of the 9th International Conference on Soft Methods in Probability and Statistics (SMPS 2018) held in Compiègne, France on September 17–21, 2018
- Provides a comprehensive overview of current research into the fusion of soft computing methods with probability and statistics
Part of the book series: Advances in Intelligent Systems and Computing (AISC, volume 832)
Conference series link(s): SMPS: International Conference Series on Soft Methods in Probability and Statistics
Conference proceedings info: SMPS 2018.
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Table of contents (29 papers)
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Front Matter
About this book
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.
Editors and Affiliations
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CNRS, Heudiasyc, Sorbonne universités, Université de technologie de Compiègne, Compiegne, France
Sébastien Destercke, Thierry Denoeux
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Department of Statistics and Operational Research and Mathematics Didactics, University of Oviedo, Oviedo, Spain
María Ángeles Gil
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Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
Przemyslaw Grzegorzewski
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Department of Stochastic Methods, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Olgierd Hryniewicz
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
DOI: https://doi.org/10.1007/978-3-319-97547-4
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-319-97546-7Published: 25 July 2018
eBook ISBN: 978-3-319-97547-4Published: 24 July 2018
Series ISSN: 2194-5357
Series E-ISSN: 2194-5365
Edition Number: 1
Number of Pages: XI, 234
Number of Illustrations: 22 b/w illustrations