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Constructing Rule-Based Models Using the Belief Functions Framework

  • Rui Jorge Almeida
  • Thierry Denoeux
  • Uzay Kaymak
Part of the Communications in Computer and Information Science book series (CCIS, volume 299)

Abstract

We study a new approach to regression analysis. We propose a new rule-based regression model using the theoretical framework of belief functions. For this purpose we use the recently proposed Evidential c-means (ECM) to derive rule-based models solely from data. ECM allocates, for each object, a mass of belief to any subsets of possible clusters, which allows to gain a deeper insight on the data while being robust with respect to outliers. The proposed rule-based models convey this added information as the examples illustrate.

Keywords

Belief Function Antecedent Variable Fuzzy Linear Regression Complete Ignorance Unreliable Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rui Jorge Almeida
    • 1
  • Thierry Denoeux
    • 2
  • Uzay Kaymak
    • 3
  1. 1.Erasmus School of EconomicsErasmus University RotterdamRotterdamThe Netherlands
  2. 2.U.M.R. C.N.R.S. 6599 HeudiasycUniversité de Technologie de CompiègneCompiègneFrance
  3. 3.School of Industrial EngineeringEindhoven University of TechnologyEindhovenThe Netherlands

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