Generalised fuzzy Bayesian Network with adaptive Vectorial Centroid

Ku Muhammad Naim Ku Khalif, Alexander Emilov Gegov

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

121 Downloads (Pure)


In this paper, the theoretical foundations of generalised fuzzy Bayesian Network based on Vectorial Centroid defuzzification is introduced. The extension of Bayesi-an Network takes a broad view by examples labelled by a fuzzy set of attributes, instead of a classical set. Com-bining fuzzy set theory and Bayesian Network’s knowledge allows the use of fuzzy variables or attrib-utes that widely used in various applications in science and engineering. It is so highlights the integration of both knowledge’s considers the need of human intuition in data analysis. Through the experimental comparison and analysis on the BUPA-liver disorder dataset, the proposed methodology is then validated theoretically and empirically.
Original languageEnglish
Title of host publication16th world congress of the international fuzzy systems association (IFSA) and the 9th conference of the European society for fuzzy logic and technology (EUSFLAT)
Place of PublicationParis
PublisherAtlantis Press
ISBN (Print)9789462520776
Publication statusPublished - 2015

Publication series

NameAdvances in intelligent systems research
PublisherAtlantis Press


  • Centroid defuzzification
  • vectorial centroid
  • bayesian network
  • human intuition


Dive into the research topics of 'Generalised fuzzy Bayesian Network with adaptive Vectorial Centroid'. Together they form a unique fingerprint.

Cite this