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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)
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Volume 27, Issue 2
Pg 169 - 273 (December 2022)
Volume 27, Issue 1
Pg 1 - 167 (June 2022)
Volume 26 (2021)
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Pg 103 - 209 (December 2021)
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Pg 1 - 101 (June 2021)
Volume 25 (2020)
Volume 25, Issue 2
Pg 85 - 142 (December 2020)
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Pg 1 - 84 (June 2020)
Volume 24 (2019)
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Pg 55 - 100 (December 2019)
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Pg 1 - 54 (June 2019)
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Pg 1 - 72 (May 2018)
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Pg 193 - 223 (December 2017)
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Pg 71 - 136 (June 2017)
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Pg 1 - 108 (February 2007)
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Pg 187 - 271 (October 2006)
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Pg 83 - 185 (June 2006)
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Pg 1 - 81 (February 2006)
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Advances in Fuzzy Sets and Systems
Advances in Fuzzy Sets and Systems
Volume 3, Issue 2, Pages 175 - 219 (June 2008)
REDUCING POSITIVE LENIENCY IN IMPORTANCE ASSESSING WITH FUZZY MEASURE RATINGS
Ting-Yu Chen (Taiwan) and Ya-Mei Peng (Taiwan)
Abstract:
The purpose of this paper is to introduce a method for measuring the importance of attribute and reducing the positive leniency error in decision analysis. Fuzzy measures have been widely used to determine the degrees of subjective importance of evaluation items. According to the principle of incompatibility, we consider the value of fuzzy measures as a linguistic value and then convert linguistic terms to fuzzy numbers. The grades of attribute importance are correspondingly expressed by fuzzy number-valued fuzzy measures. However, the leniency error may exist when most attributes are assigned unduly high ratings. Because respondents often assign similarly complimentary scores, errors of positive leniency make it difficult to differentiate the importance of decision attributes. To reduce positive leniency error in ratings, we use fuzzy distance measures to adjust the distance between attributes. The average values of attribute importance, calculated by the typical fuzzy measures and the typical fuzzy number-valued fuzzy measures in the triangular fuzzy numbers, are regarded as the basic outcomes and compared with the fuzzy measures and the fuzzy number-valued fuzzy measures which are characterized by the distance measures. The results of the pair-samples
T
test and the skewness indicate that fuzzy measures with distance measures and fuzzy number-valued fuzzy measures with distance measures cannot only adjust the distance between attributes, but also improve the overestimated rating of attribute importance, especially under the condition that an individual makes a decision on a complex decision-making and high-involvement product. Furthermore, the Spearman correlation coefficient, a performance index, demonstrates that measures with distance measures can obtain more accurate preference orders than those measures without distance measures to adjust the span between two attributes.
Keywords and phrases:
positive leniency error, attribute importance, fuzzy measure, fuzzy number-valued fuzzy measure, distance measure.
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P-ISSN: 0973-421X
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