Generalization and extension of partitioned Bonferroni mean operator to model optional prerequisites. Issue 5 (3rd March 2020)

• Record Type: Journal Article
• Authors: Hait, Swati Rani; Guha, Debashree; Chakraborty, Debjani; Mesiar, Radko
• Is Part Of: International journal of intelligent systems. Volume 35:Issue 5(2020)
• Page Start: 891
• Page End: 919
• Publication Date: 2020-03-03
• Subjects: aggregation operator -- Bonferroni mean -- generalized partitioned Bonferroni mean -- optional partitioned Bonferroni mean -- partitioned Bonferroni mean; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22229
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: The partitioned Bonferroni mean (PBM) operator, which was oriented as an elementary attempt to outstretch the Bonferroni mean (BM) operator, has enlarged the class of BM‐type aggregation operators for information accumulation by modeling interrelationship among pairwise disjoint partition sets with the presupposition that the criteria of intra‐partition are homogeneously related to each other, while no relationship exists among criteria of inter‐partition. Although PBM has encountered a lot of attraction from the researchers due to its versatility in information aggregation technique, the principal disadvantage of the existing PBM definitions evolution is that they do not provide any specification regarding the relationship among criteria of partition structure during design, development, and applications of PBM over unalike situations of information fusion. This consideration propels us to focus on the systematic investigation of different variations of PBM operators based on various mandatory requisites to be imposed on information retrieved from the partition sets. In this regard, we propose the construction of novel generalized partitioned Bonferroni mean (GPBM) operator by befitting its suitable components to provide a descriptive configuration, which is quite interpretable, understandable and thus facilitates the ability to model specific mandatory prerequisites in a single operator. To enrich the capacity for modeling real‐life decision situations, the PBM operator is customized to propose optional partitioned Bonferroni mean (OPBM) operator that captures partition‐wise interrelationship among attributes while taking into consideration optional conditions jumbled in each partition set. Furthermore, we demonstrate the construction methodology of generalized OPBM operator that amalgamate the concept of GPBM and OPBM operator to enhance and model‐specific requirements along with optional requirements as per the desires of decision makers.