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Synergies of Soft Computing and Statistics for Intelligent Data Analysis

  • Conference proceedings
  • © 2013

Overview

Editors:
  1. Rudolf Kruse

    1. Faculty of Computer Science, Otto-von-Guericke University of Magdebur, Magdeburg, Germany

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  2. Michael R. Berthold

    1. , FB Informatik & Informationswissenschaft, University of Konstanz, Konstanz, Germany

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  3. Christian Moewes

    1. of Magdeburg, Faculty of Computer Science, Otto-von-Guericke University, Magdeburg, Germany

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  4. María Ángeles Gil

    1. , Department of Statistics and OR, University of Oviedo, Oviedo, Spain

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  5. Przemysław Grzegorzewski

    1. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

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  6. Olgierd Hryniewicz

    1. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

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  • Presents the results of the International Conference Series on Soft Methods in Probability and Statistics SMPS'2012 held in Konstanz, Germany, October 4-6,2012
  • Presents recent results illustrating new trends in intelligent data analysis
  • Gives 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 190)

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Table of contents (61 papers)

  1. Front Matter

    Pages 1-14
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  2. Invited Papers

    1. Front Matter

      Pages 1-1
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    2. Soft Pattern Mining in Neuroscience

      • Christian Borgelt
      Pages 3-10

    3. Exploring Big Data with Scalable Soft Clustering

      • Lawrence O. Hall
      Pages 11-15

    4. On Creative Uses of Word Associations

      • Hannu Toivonen, Oskar Gross, Jukka M. Toivanen, Alessandro Valitutti
      Pages 17-24

  3. Foundations

    1. Front Matter

      Pages 25-25
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  4. Foundations

    1. Combining Imprecise Probability Masses with Maximal Coherent Subsets: Application to Ensemble Classification

      • Sébastien Destercke, Violaine Antoine
      Pages 27-35

    2. The Goodman-Nguyen Relation in Uncertainty Measurement

      • Renato Pelessoni, Paolo Vicig
      Pages 37-44

    3. The CONEstrip Algorithm

      • Erik Quaeghebeur
      Pages 45-54

    4. Towards a Robust Imprecise Linear Deconvolution

      • Oliver Strauss, Agnes Rico
      Pages 55-62

    5. 2D Probability-Possibility Transformations

      • Alessandro Ferrero, Marco Prioli, Simona Salicone, Barbara Vantaggi
      Pages 63-72

    6. A Note on the Convex Structure of Uncertainty Measures on MV-algebras

      • Tommaso Flaminio, Lluís Godo Lacasa
      Pages 73-81

    7. Cumulative Distribution Function Estimation with Fuzzy Data: Some Estimators and Further Problems

      • Xuecheng Liu, Shoumei Li
      Pages 83-91

    8. On Equations with a Fuzzy Stochastic Integral with Respect to Semimartingales

      • Marek T. Malinowski
      Pages 93-101

  5. Statistical Methods

    1. Front Matter

      Pages 103-103
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    2. A Linear Regression Model for Interval-Valued Response Based on Set Arithmetic

      • Angela Blanco-Fernández, Ana Colubi, Marta García-Bárzana, Manuel Montenegro
      Pages 105-113

    3. A Proposal of Robust Regression for Random Fuzzy Sets

      • Maria Brigida Ferraro, Paolo Giordani
      Pages 115-123

    4. Bootstrap Comparison of Statistics for Testing the Homoscedasticity of Random Fuzzy Sets

      • Ana Belén Ramos-Guajardo, María Asunción Lubiano, Gil González-Rodríguez
      Pages 125-133

    5. Fuzzy Rating vs. Fuzzy Conversion Scales: An Empirical Comparison through the MSE

      • Sara de la Rosa de Sáa, María Ángeles Gil, María Teresa López García, María Asunción Lubiano
      Pages 135-143

    6. A Law of Large Numbers for Exchangeable Random Variables on Nonadditive Measures

      • Li Guan, Shoumei Li
      Pages 145-152
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Keywords

About this book

In recent years there has been a growing interest to extend classical methods for data analysis.
The aim is to allow a more flexible modeling of phenomena such as uncertainty, imprecision or ignorance.
Such extensions of classical probability theory and statistics are useful in many real-life situations, since uncertainties in data are not only present in the form of randomness --- various types of incomplete or subjective information have to be handled.
About twelve years ago the idea of strengthening the dialogue between the various research communities in the field of data analysis was born and resulted in the International Conference Series on Soft Methods in Probability and Statistics (SMPS).

This book gathers contributions presented at the SMPS'2012 held in Konstanz, Germany. Its aim is to present recent results illustrating new trends in intelligent data analysis.
It gives a comprehensive overview of current research into the fusion of soft computing methods with probability and statistics.
Synergies of both fields might improve intelligent data analysis methods in terms of robustness to noise and applicability to larger datasets, while being able to efficiently obtain understandable solutions of real-world problems.

Editors and Affiliations

  • Faculty of Computer Science, Otto-von-Guericke University of Magdebur, Magdeburg, Germany

    Rudolf Kruse

  • , FB Informatik & Informationswissenschaft, University of Konstanz, Konstanz, Germany

    Michael R. Berthold

  • of Magdeburg, Faculty of Computer Science, Otto-von-Guericke University, Magdeburg, Germany

    Christian Moewes

  • , Department of Statistics and OR, University of Oviedo, Oviedo, Spain

    María Ángeles Gil

  • Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Przemysław Grzegorzewski, Olgierd Hryniewicz

Bibliographic Information

  • Book Title: Synergies of Soft Computing and Statistics for Intelligent Data Analysis

  • Editors: Rudolf Kruse, Michael R. Berthold, Christian Moewes, María Ángeles Gil, Przemysław Grzegorzewski, Olgierd Hryniewicz

  • Series Title: Advances in Intelligent Systems and Computing

  • DOI: https://doi.org/10.1007/978-3-642-33042-1

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2013

  • Softcover ISBN: 978-3-642-33041-4Published: 07 September 2012

  • eBook ISBN: 978-3-642-33042-1Published: 13 September 2012

  • Series ISSN: 2194-5357

  • Series E-ISSN: 2194-5365

  • Edition Number: 1

  • Number of Pages: XVI, 584

  • Number of Illustrations: 74 b/w illustrations

  • Topics: Computational Intelligence, Artificial Intelligence

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