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An alternate unsupervised technique based on distance correlation and shannon entropy to estimate λ⁰ -fuzzy measure

λ⁰-measure is a special type of fuzzy measure. In the context of multi-attribute decision making (MADM), the measure can be used together with Choquet integral to model the interdependencies that usually present between the decision attributes. Unfortunately, the range of techniques available to est...

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Main Authors: Anath Rau Krishnan, Maznah Mat Kasim, Rizal Hamid
Format: Article
Language:English
Published: MDPI AG 2020
Subjects:
QA75.5-76.95 Electronic computers. Computer science
QC1-75 General
Online Access:https://eprints.ums.edu.my/id/eprint/42464/1/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/42464/
http://dx.doi.org/10.3390/sym12101708
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spelling my.ums.eprints.424642024-12-31T01:21:09Z https://eprints.ums.edu.my/id/eprint/42464/ An alternate unsupervised technique based on distance correlation and shannon entropy to estimate λ⁰ -fuzzy measure Anath Rau Krishnan Maznah Mat Kasim Rizal Hamid QA75.5-76.95 Electronic computers. Computer science QC1-75 General λ⁰-measure is a special type of fuzzy measure. In the context of multi-attribute decision making (MADM), the measure can be used together with Choquet integral to model the interdependencies that usually present between the decision attributes. Unfortunately, the range of techniques available to estimate λ⁰-measure values is too limited i.e., only four techniques are available to this date. Besides, the review on literature shows that each of these existing techniques either requires some initial data from the decision-makers or misrepresents the actual interdependencies held by the attributes. Thus, an alternate unsupervised technique is needed for the estimation of λ⁰-measure values. This study has developed such a technique by integrating the idea of distance correlation and Shannon entropy. In this technique, the two inputs required to estimate λ⁰-measure values, namely, the interdependence degrees and fuzzy densities are determined by utilizing the distance correlation measures and entropy weights, respectively. An evaluation to rank the websites owned by five different hospitals located in Sabah, Malaysia, was conducted to illustrate the usage of the technique. A similar evaluation was also performed with a few selected MADM techniques for comparison purposes, where the proposed technique is found to have produced the most consistent ranking. From the literature perspective, this study has contributed an alternate unsupervised technique that can estimate λ⁰-measure values without necessitating any additional data from the decision-makers, and at the same time can better capture the interdependencies held by the attributes. MDPI AG 2020 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/42464/1/FULL%20TEXT.pdf Anath Rau Krishnan and Maznah Mat Kasim and Rizal Hamid (2020) An alternate unsupervised technique based on distance correlation and shannon entropy to estimate λ⁰ -fuzzy measure. Symmetry, 12. pp. 1-24. http://dx.doi.org/10.3390/sym12101708
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
topic QA75.5-76.95 Electronic computers. Computer science
QC1-75 General
spellingShingle QA75.5-76.95 Electronic computers. Computer science
QC1-75 General
Anath Rau Krishnan
Maznah Mat Kasim
Rizal Hamid
An alternate unsupervised technique based on distance correlation and shannon entropy to estimate λ⁰ -fuzzy measure
description λ⁰-measure is a special type of fuzzy measure. In the context of multi-attribute decision making (MADM), the measure can be used together with Choquet integral to model the interdependencies that usually present between the decision attributes. Unfortunately, the range of techniques available to estimate λ⁰-measure values is too limited i.e., only four techniques are available to this date. Besides, the review on literature shows that each of these existing techniques either requires some initial data from the decision-makers or misrepresents the actual interdependencies held by the attributes. Thus, an alternate unsupervised technique is needed for the estimation of λ⁰-measure values. This study has developed such a technique by integrating the idea of distance correlation and Shannon entropy. In this technique, the two inputs required to estimate λ⁰-measure values, namely, the interdependence degrees and fuzzy densities are determined by utilizing the distance correlation measures and entropy weights, respectively. An evaluation to rank the websites owned by five different hospitals located in Sabah, Malaysia, was conducted to illustrate the usage of the technique. A similar evaluation was also performed with a few selected MADM techniques for comparison purposes, where the proposed technique is found to have produced the most consistent ranking. From the literature perspective, this study has contributed an alternate unsupervised technique that can estimate λ⁰-measure values without necessitating any additional data from the decision-makers, and at the same time can better capture the interdependencies held by the attributes.
format Article
author Anath Rau Krishnan
Maznah Mat Kasim
Rizal Hamid
author_facet Anath Rau Krishnan
Maznah Mat Kasim
Rizal Hamid
author_sort Anath Rau Krishnan
title An alternate unsupervised technique based on distance correlation and shannon entropy to estimate λ⁰ -fuzzy measure
title_short An alternate unsupervised technique based on distance correlation and shannon entropy to estimate λ⁰ -fuzzy measure
title_full An alternate unsupervised technique based on distance correlation and shannon entropy to estimate λ⁰ -fuzzy measure
title_fullStr An alternate unsupervised technique based on distance correlation and shannon entropy to estimate λ⁰ -fuzzy measure
title_full_unstemmed An alternate unsupervised technique based on distance correlation and shannon entropy to estimate λ⁰ -fuzzy measure
title_sort alternate unsupervised technique based on distance correlation and shannon entropy to estimate λ⁰ -fuzzy measure
publisher MDPI AG
publishDate 2020
url https://eprints.ums.edu.my/id/eprint/42464/1/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/42464/
http://dx.doi.org/10.3390/sym12101708
_version_ 1821003220941537280
score 13.244413

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