Bayesian logistic regression using vectorial centroid for interval type-2 fuzzy sets

Ku Muhammad Naim Ku Khalif, Alexander Emilov Gegov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

It is necessary to represent the probabilities of fuzzy events based on a Bayesian knowledge. Inspired by such real applications, in this research study, the theoretical foundations of Vectorial Centroid of interval type-2 fuzzy sets with Bayesian logistic regression is introduced. This includes official models, elementary operations, basic properties and advanced application. The Vectorial Centroid method for interval type-2 fuzzy set takes a broad view by exampled labelled by a classical Vectorial Centroid defuzzification method for type-1 fuzzy sets. Rather than using type-1 fuzzy sets for implementing fuzzy events, type-2 fuzzy sets are recommended based on the involvement of uncertainty quantity. It also highlights the incorporation of fuzzy sets with Bayesian logistic regression allows the use of fuzzy attributes by considering the need of human intuition in data analysis. It is worth adding here that this proposed methodology then applied for BUPA liver-disorder dataset and val idated theoretically and empirically.
Original languageEnglish
Title of host publicationProceedings of the 7th international joint conference on computational intelligence
Subtitle of host publicationfuzzy computational theory and applications
Publisher SCITEPRESS – Science and Technology Publications
Pages69-79
Number of pages11
Volume2
ISBN (Print)978-989-758-157-1
DOIs
Publication statusPublished - Nov 2015
Event7th International Joint Conference on Computational Intelligence - Lisbon, Portugal
Duration: 12 Nov 201514 Nov 2015

Conference

Conference7th International Joint Conference on Computational Intelligence
Country/TerritoryPortugal
CityLisbon
Period12/11/1514/11/15

Keywords

  • Interval Type-2 Fuzzy Sets
  • Uncertainty
  • Defuzzification
  • Vectorial Centroid
  • Machine Learning
  • Bayesian Logistic Regression
  • Human Intuition

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