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Authors: Ku Muhammad Naim Ku Khalif and Alexander Gegov

Affiliation: University of Portsmouth, United Kingdom

ISBN: 978-989-758-157-1

Keyword(s): Interval Type-2 Fuzzy Sets, Uncertainty, Defuzzification, Vectorial Centroid, Machine Learning, Bayesian Logistic Regression, Human Intuition.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Fuzzy Systems ; Soft Computing ; Type-2 Fuzzy Logic

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. (More)

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Paper citation in several formats:
Ku Khalif, K. and Gegov, A. (2015). Bayesian Logistic Regression using Vectorial Centroid for Interval Type-2 Fuzzy Sets.In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 2 FCTA: FCTA, (ECTA 2015) ISBN 978-989-758-157-1, pages 69-79. DOI: 10.5220/0005614400690079

@conference{fcta15,
author={Ku Muhammad Naim Ku Khalif. and Alexander Gegov.},
title={Bayesian Logistic Regression using Vectorial Centroid for Interval Type-2 Fuzzy Sets},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 2 FCTA: FCTA, (ECTA 2015)},
year={2015},
pages={69-79},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005614400690079},
isbn={978-989-758-157-1},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 2 FCTA: FCTA, (ECTA 2015)
TI - Bayesian Logistic Regression using Vectorial Centroid for Interval Type-2 Fuzzy Sets
SN - 978-989-758-157-1
AU - Ku Khalif, K.
AU - Gegov, A.
PY - 2015
SP - 69
EP - 79
DO - 10.5220/0005614400690079

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