Implementing adaptive vectorial centroid in Bayesian logistic regression for interval type-2 fuzzy sets

Ku Muhammad Naim Ku Khalif, Alexander Gegov

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

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A prior distributions in standard Bayesian knowledge are assumed to be classical probability distribution. It is required to representable those probabilities of fuzzy events based on Bayesian knowledge. Propelled by such real applications, in this research study, the theoretical foundations of Vectorial Centroid of interval type-2 fuzzy set with Bayesian logistic regression is introduced. As opposed of utilising type-1 fuzzy set, type-2 fuzzy set is recommended based on the involvement of uncertainty quantity. It additionally highlights the association of fuzzy sets with Bayesian logistic regression permits the use of fuzzy attributes by considering the need of human intuition in data analysis. It may be worth including here that this proposed methodology then applied for BUPA liver-disorder dataset and validated theoretically and empirically.
Original languageEnglish
Title of host publicationComputational Intelligence
Subtitle of host publicationInternational Joint Conference, IJCCI 2015 Lisbon, Portugal, November 12-14, 2015, Revised Selected Papers
EditorsJuan Merelo, Agostinho Rosa, Jose Cadenas, Antonio Correia, Kurosh Madani, Antonio Ruano, Joaquim Filipe
Number of pages19
ISBN (Electronic)978-3319485065
ISBN (Print)978-3319485041
Publication statusPublished - 24 Nov 2016
EventInternational Joint Conference on Computational Intelligence, IJCCI 2015 - Libson, Portugal
Duration: 12 Nov 201514 Nov 2015

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


ConferenceInternational Joint Conference on Computational Intelligence, IJCCI 2015


  • machine learning
  • Bayesian logistic regression
  • interval type-2 fuzzy set
  • defuzzification
  • vectorial centroid
  • human intuition
  • uncertainty


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