Please use this identifier to cite or link to this item: https://doi.org/10.2991/ijcis.d.190930.002
Title: Aggregating interrelated attributes in multi-attribute decision-making with ELICIT information based on bonferroni mean and its variants
Authors: Dutta, B. 
Labella, Á.
Rodríguez, R.M.
Martínez, L.
Keywords: Aggregation operator
Bonferroni mean
ELICIT information
Interrelationship
Issue Date: 2019
Publisher: Atlantis Press
Citation: Dutta, B., Labella, Á., Rodríguez, R.M., Martínez, L. (2019). Aggregating interrelated attributes in multi-attribute decision-making with ELICIT information based on bonferroni mean and its variants. International Journal of Computational Intelligence Systems 12 (2) : 1179-1196. ScholarBank@NUS Repository. https://doi.org/10.2991/ijcis.d.190930.002
Rights: Attribution-NonCommercial 4.0 International
Abstract: In recent times, to improve the interpretability and accuracy of computing with words processes, a rich linguistic representation model has been developed and referred to as Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT). This model extends the definition of the comparative linguistic expressions into a continuous domain due to the use of the symbolic translation concept related to the 2-tuple linguistic model. The aggregation of ELICIT information via a suitable rule that reflects the underlying interrelation among the aggregated information in output is the key tool to design decision-making algorithm for solving multi-attribute decision-making problems under linguistic information. In this study, we introduce three aggregation operators for aggregating ELICIT information in aim of capturing three different types of interrelationship patterns among inputs, which we refer to as ELICIT Bonferroni mean, ELICIT extended Bonferroni mean and ELICIT partitioned Bonferroni mean. Further, the key aggregation properties of these proposed operators are investigated with the proposal of weighted forms. Based on the proposed aggregation operators, an approach for solving multi-attribute decision-making problems, in which attributes are interrelated is developed. Finally, a didactic example is presented to illustrate the working of the proposal and demonstrate its feasibility. © 2019 The Authors. Published by Atlantis Press SARL.
Source Title: International Journal of Computational Intelligence Systems
URI: https://scholarbank.nus.edu.sg/handle/10635/212343
ISSN: 18756891
DOI: 10.2991/ijcis.d.190930.002
Rights: Attribution-NonCommercial 4.0 International
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