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Volume 29 (2024)
Volume 29, Issue 1 (In Progress)
Pg 1 - 37 (June 2024)
Volume 28 (2023)
Volume 28, Issue 2
Pg 65 - 119 (December 2023)
Volume 28, Issue 1
Pg 1 - 64 (June 2023)
Volume 27 (2022)
Volume 27, Issue 2
Pg 169 - 273 (December 2022)
Volume 27, Issue 1
Pg 1 - 167 (June 2022)
Volume 26 (2021)
Volume 26, Issue 2
Pg 103 - 209 (December 2021)
Volume 26, Issue 1
Pg 1 - 101 (June 2021)
Volume 25 (2020)
Volume 25, Issue 2
Pg 85 - 142 (December 2020)
Volume 25, Issue 1
Pg 1 - 84 (June 2020)
Volume 24 (2019)
Volume 24, Issue 2
Pg 55 - 100 (December 2019)
Volume 24, Issue 1
Pg 1 - 54 (June 2019)
Volume 23 (2018)
Volume 23, Issue 2-3
Pg 73 - 127 (November 2018)
Volume 23, Issue 1
Pg 1 - 72 (May 2018)
Volume 22 (2017)
Volume 22, Issue 4
Pg 193 - 223 (December 2017)
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Pg 137 - 191 (September 2017)
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Pg 71 - 136 (June 2017)
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Pg 1 - 70 (March 2017)
Volume 21 (2016)
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Pg 265 - 315 (December 2016)
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Pg 187 - 264 (September 2016)
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Pg 107 - 185 (June 2016)
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Pg 1 - 86 (September 2014)
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Pg 1 - 93 (March 2014)
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Pg 65 - 123 (December 2013)
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Pg 1 - 64 (October 2013)
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Pg 73 - 111 (August 2013)
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Pg 1 - 71 (June 2013)
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Pg 71 - 132 (April 2013)
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Pg 1 - 69 (February 2013)
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Pg 77 - 142 (December 2012)
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Pg 1 - 75 (October 2012)
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Pg 79 - 143 (August 2012)
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Pg 1 - 78 (June 2012)
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Pg 65 - 129 (April 2012)
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Pg 1 - 64 (February 2012)
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Pg 77 - 139 (December 2011)
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Pg 1 - 75 (October 2011)
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Pg 93 - 197 (August 2011)
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Pg 1 - 91 (June 2011)
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Pg 1 - 60 (February 2011)
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Pg 1 - 59 (October 2010)
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Pg 95 - 179 (August 2010)
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Pg 1 - 94 (June 2010)
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Pg 1 - 80 (February 2010)
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Pg 1 - 120 (February 2009)
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Pg 255 - 387 (October 2008)
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Pg 131 - 254 (June 2008)
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Pg 1 - 129 (February 2008)
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Volume 2, Issue 3
Pg 217 - 320 (October 2007)
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Pg 109 - 216 (June 2007)
Volume 2, Issue 1
Pg 1 - 108 (February 2007)
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Volume 1, Issue 3
Pg 187 - 271 (October 2006)
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Pg 83 - 185 (June 2006)
Volume 1, Issue 1
Pg 1 - 81 (February 2006)
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Advances in Fuzzy Sets and Systems
Advances in Fuzzy Sets and Systems
Volume 6, Issue 1, Pages 77 - 94 (June 2010)
ASSESSING INFORMATIONAL IMPORTANCE BY USING INTUITIONISTIC FUZZY ENTROPY MEASURES AND EXPERIMENTAL ANALYSIS
Ting-Yu Chen and Chia-Hang Li
Abstract:
In Multiple Attribute Decision Making (MADM), it is important to properly assess the weight of attributes, because different weight results would often cause entirely different decision results. After the theory of Intuitionistic Fuzzy (IF) sets is applied to solve MADM problems, it causes the decision matrix get more complex and contains more uncertainty, and therefore it is important to make sure of the credibility of data itself. However, there is little investigation on MADM with the credibility of data being explicitly taken into account in the past. In our research, we propose a new informational importance method by using IF entropy measures for MADM under intuitionistic fuzzy environment. We utilize the nature of IF entropy to assess the objective weight of attributes based on the credibility of data, and the concept is totally different with the traditional one. Moreover, there are many IF entropy measures which are originated with different theories. We investigate the differences among those varied measures through the experimental analysis. According to the experiment results, the differences undoubtedly exist among those measures. Even the measures which are all originated from the same theory also contain variation among them. Besides, we also understand the number of attributes and alternatives would influence the degree of difference among those measures. Our method combines with the traditional one to get a better compromise weighting method which contains both advantages of traditional and new methods.
Keywords and phrases:
decision making, intuitionistic fuzzy sets, intuitionistic fuzzy entropy, informational importance, objective weight, experimental analysis.
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P-ISSN: 0973-421X
Journal Stats
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Citation count (Google Scholar):
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h10-index (Google Scholar):
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h-index (Google Scholar):
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Downloads :
88331
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