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

Ku Muhammad Naim Ku Khalif; Alexander Gegov

doi:10.1007/978-3-319-48506-5_16

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

Ku Muhammad Naim Ku Khalif, Alexander Gegov

School of Computing

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

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Abstract

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 language	English
Title of host publication	Computational Intelligence
Subtitle of host publication	International Joint Conference, IJCCI 2015 Lisbon, Portugal, November 12-14, 2015, Revised Selected Papers
Editors	Juan Merelo, Agostinho Rosa, Jose Cadenas, Antonio Correia, Kurosh Madani, Antonio Ruano, Joaquim Filipe
Publisher	Springer
Pages	315-333
Number of pages	19
Edition	1
ISBN (Electronic)	978-3319485065
ISBN (Print)	978-3319485041
DOIs	https://doi.org/10.1007/978-3-319-48506-5_16
Publication status	Published - 24 Nov 2016
Event	International Joint Conference on Computational Intelligence, IJCCI 2015 - Libson, Portugal Duration: 12 Nov 2015 → 14 Nov 2015

Publication series

Name	Studies in Computational Intelligence
Publisher	Springer
Volume	669
ISSN (Print)	1860-949X

Conference

Conference	International Joint Conference on Computational Intelligence, IJCCI 2015
Country/Territory	Portugal
City	Libson
Period	12/11/15 → 14/11/15

Keywords

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

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10.1007/978-3-319-48506-5_16

Implementing adaptive vectorial centroid post-print
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-48506-5_16
Accepted author manuscript (Post-print), 529 KBLicence: Unspecified

Cite this

Ku Khalif, K. M. N., & Gegov, A. (2016). Implementing adaptive vectorial centroid in Bayesian logistic regression for interval type-2 fuzzy sets. In J. Merelo, A. Rosa, J. Cadenas, A. Correia, K. Madani, A. Ruano, & J. Filipe (Eds.), Computational Intelligence: International Joint Conference, IJCCI 2015 Lisbon, Portugal, November 12-14, 2015, Revised Selected Papers (1 ed., pp. 315-333). (Studies in Computational Intelligence; Vol. 669). Springer. https://doi.org/10.1007/978-3-319-48506-5_16

Ku Khalif, Ku Muhammad Naim ; Gegov, Alexander. / Implementing adaptive vectorial centroid in Bayesian logistic regression for interval type-2 fuzzy sets. Computational Intelligence: International Joint Conference, IJCCI 2015 Lisbon, Portugal, November 12-14, 2015, Revised Selected Papers. editor / Juan Merelo ; Agostinho Rosa ; Jose Cadenas ; Antonio Correia ; Kurosh Madani ; Antonio Ruano ; Joaquim Filipe. 1. ed. Springer, 2016. pp. 315-333 (Studies in Computational Intelligence).

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title = "Implementing adaptive vectorial centroid in Bayesian logistic regression for interval type-2 fuzzy sets",

abstract = "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.",

keywords = "machine learning, Bayesian logistic regression, interval type-2 fuzzy set, defuzzification, vectorial centroid, human intuition, uncertainty",

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

year = "2016",

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Ku Khalif, KMN & Gegov, A 2016, Implementing adaptive vectorial centroid in Bayesian logistic regression for interval type-2 fuzzy sets. in J Merelo, A Rosa, J Cadenas, A Correia, K Madani, A Ruano & J Filipe (eds), Computational Intelligence: International Joint Conference, IJCCI 2015 Lisbon, Portugal, November 12-14, 2015, Revised Selected Papers. 1 edn, Studies in Computational Intelligence, vol. 669, Springer, pp. 315-333, International Joint Conference on Computational Intelligence, IJCCI 2015, Libson, Portugal, 12/11/15. https://doi.org/10.1007/978-3-319-48506-5_16

Implementing adaptive vectorial centroid in Bayesian logistic regression for interval type-2 fuzzy sets. / Ku Khalif, Ku Muhammad Naim; Gegov, Alexander.
Computational Intelligence: International Joint Conference, IJCCI 2015 Lisbon, Portugal, November 12-14, 2015, Revised Selected Papers. ed. / Juan Merelo; Agostinho Rosa; Jose Cadenas; Antonio Correia; Kurosh Madani; Antonio Ruano; Joaquim Filipe. 1. ed. Springer, 2016. p. 315-333 (Studies in Computational Intelligence; Vol. 669).

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

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T1 - Implementing adaptive vectorial centroid in Bayesian logistic regression for interval type-2 fuzzy sets

AU - Ku Khalif, Ku Muhammad Naim

AU - Gegov, Alexander

PY - 2016/11/24

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N2 - 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.

AB - 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.

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KW - Bayesian logistic regression

KW - interval type-2 fuzzy set

KW - defuzzification

KW - vectorial centroid

KW - human intuition

KW - uncertainty

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A2 - Filipe, Joaquim

PB - Springer

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Ku Khalif KMN, Gegov A. Implementing adaptive vectorial centroid in Bayesian logistic regression for interval type-2 fuzzy sets. In Merelo J, Rosa A, Cadenas J, Correia A, Madani K, Ruano A, Filipe J, editors, Computational Intelligence: International Joint Conference, IJCCI 2015 Lisbon, Portugal, November 12-14, 2015, Revised Selected Papers. 1 ed. Springer. 2016. p. 315-333. (Studies in Computational Intelligence). doi: 10.1007/978-3-319-48506-5_16

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

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