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

Ku Muhammad Naim Ku Khalif; Alexander Emilov Gegov

doi:10.5220/0005614400690079

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

Ku Muhammad Naim Ku Khalif, Alexander Emilov Gegov

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings of the 7th international joint conference on computational intelligence
Subtitle of host publication	fuzzy computational theory and applications
Editors	Antonio Dourado, Antonio Ruano, Agostinho Rosa, Kurosh Madani, Joaquim Filipe, Jose M. Cadenas, Juan Julian Merelo, Joaquim Filipe
Publisher	SciTePress
Pages	69-79
Number of pages	11
Volume	2
ISBN (Print)	978-989-758-157-1
DOIs	https://doi.org/10.5220/0005614400690079
Publication status	Published - Nov 2015
Event	7th International Joint Conference on Computational Intelligence - Lisbon, Portugal Duration: 12 Nov 2015 → 14 Nov 2015

Conference

Conference	7th International Joint Conference on Computational Intelligence
Country/Territory	Portugal
City	Lisbon
Period	12/11/15 → 14/11/15

Keywords

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

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Ku Khalif, K. M. N., & Gegov, A. E. (2015). Bayesian logistic regression using vectorial centroid for interval type-2 fuzzy sets. In A. Dourado, A. Ruano, A. Rosa, K. Madani, J. Filipe, J. M. Cadenas, J. J. Merelo, & J. Filipe (Eds.), Proceedings of the 7th international joint conference on computational intelligence: fuzzy computational theory and applications (Vol. 2, pp. 69-79). SciTePress. https://doi.org/10.5220/0005614400690079

Ku Khalif, Ku Muhammad Naim ; Gegov, Alexander Emilov. / Bayesian logistic regression using vectorial centroid for interval type-2 fuzzy sets. Proceedings of the 7th international joint conference on computational intelligence: fuzzy computational theory and applications. editor / Antonio Dourado ; Antonio Ruano ; Agostinho Rosa ; Kurosh Madani ; Joaquim Filipe ; Jose M. Cadenas ; Juan Julian Merelo ; Joaquim Filipe. Vol. 2 SciTePress, 2015. pp. 69-79

@inproceedings{a8800de9a0c04cffb7d87574f63be79a,

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 val idated theoretically and empirically. ",

keywords = " Interval Type-2 Fuzzy Sets, Uncertainty, Defuzzification, Vectorial Centroid, Machine Learning, Bayesian Logistic Regression, Human Intuition",

author = "{Ku Khalif}, {Ku Muhammad Naim} and Gegov, {Alexander Emilov}",

year = "2015",

month = nov,

doi = "10.5220/0005614400690079",

language = "English",

isbn = "978-989-758-157-1",

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editor = "Antonio Dourado and Antonio Ruano and Agostinho Rosa and Kurosh Madani and Joaquim Filipe and Cadenas, {Jose M.} and Merelo, {Juan Julian} and Joaquim Filipe",

booktitle = "Proceedings of the 7th international joint conference on computational intelligence",

publisher = "SciTePress",

note = "7th International Joint Conference on Computational Intelligence ; Conference date: 12-11-2015 Through 14-11-2015",

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Ku Khalif, KMN & Gegov, AE 2015, Bayesian logistic regression using vectorial centroid for interval type-2 fuzzy sets. in A Dourado, A Ruano, A Rosa, K Madani, J Filipe, JM Cadenas, JJ Merelo & J Filipe (eds), Proceedings of the 7th international joint conference on computational intelligence: fuzzy computational theory and applications. vol. 2, SciTePress, pp. 69-79, 7th International Joint Conference on Computational Intelligence, Lisbon, Portugal, 12/11/15. https://doi.org/10.5220/0005614400690079

Bayesian logistic regression using vectorial centroid for interval type-2 fuzzy sets. / Ku Khalif, Ku Muhammad Naim; Gegov, Alexander Emilov.
Proceedings of the 7th international joint conference on computational intelligence: fuzzy computational theory and applications. ed. / Antonio Dourado; Antonio Ruano; Agostinho Rosa; Kurosh Madani; Joaquim Filipe; Jose M. Cadenas; Juan Julian Merelo; Joaquim Filipe. Vol. 2 SciTePress, 2015. p. 69-79.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Ku Khalif, Ku Muhammad Naim

AU - Gegov, Alexander Emilov

PY - 2015/11

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N2 - 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.

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KW - Human Intuition

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BT - Proceedings of the 7th international joint conference on computational intelligence

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A2 - Merelo, Juan Julian

A2 - Filipe, Joaquim

PB - SciTePress

T2 - 7th International Joint Conference on Computational Intelligence

Y2 - 12 November 2015 through 14 November 2015

ER -

Ku Khalif KMN, Gegov AE. Bayesian logistic regression using vectorial centroid for interval type-2 fuzzy sets. In Dourado A, Ruano A, Rosa A, Madani K, Filipe J, Cadenas JM, Merelo JJ, Filipe J, editors, Proceedings of the 7th international joint conference on computational intelligence: fuzzy computational theory and applications. Vol. 2. SciTePress. 2015. p. 69-79 doi: 10.5220/0005614400690079

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

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