Paper Info
Title
Bayesian Logistic Regression using Vectorial Centroid for Interval Type-2 Fuzzy Sets.
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 validated theoretically and empirically.
Year
DOI
Venue
2015
10.5220/0005614400690079
IJCCI (FCTA)
Keywords
Field
DocType
Interval Type-2 Fuzzy Sets,Uncertainty,Defuzzification,Vectorial Centroid,Machine Learning,Bayesian Logistic Regression,Human Intuition
Neuro-fuzzy,Fuzzy classification,Defuzzification,Pattern recognition,Fuzzy set operations,Fuzzy logic,Fuzzy set,Artificial intelligence,Fuzzy number,Membership function,Mathematics
Conference
Volume
ISBN
Citations 
2
978-1-5090-1968-7
0
PageRank 
References 
Authors
0.34
9
2
Name
Order
Citations
PageRank
Ku Muhammad Naim Ku Khalif152.13
Alexander E. Gegov2478.47