A new consensus mining approach to group ranking problems involving different intensities of preferences. (May 2019)

• Record Type: Journal Article
• Authors: Ma, Li-Ching
• Is Part Of: Computers & industrial engineering. Volume 131(2019)
• Page Start: 320
• Page End: 326
• Publication Date: 2019-05
• Subjects: Group decision and negotiations -- Data mining -- Group ranking -- Consensus mining -- Borda majority count; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2019.04.001
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A total ranking list involving group consensus preferences is achieved. A group consensus mining approach is proposed without candidate generation. An optimization model involving maximum consensus sequences is developed. Flexibility is provided in solving ranking problems using different input formats. Minimum consensus levels and maximum disagreement levels are adjustable. Abstract: Discovering the group priority from a set of user preferences plays an important role in group decision making because of its extensive applications in practice. In most group ranking problems, users are supposed to input a ranking of alternatives regardless of the intensities of preference. However, if two users specify that they prefer A to B, the intensities of preference may be quite different. In addition, most researchers have tried to determine a total ranking list by minimizing total differences among user preferences, but users might have little consensus on the final results. This study aims to propose a new consensus-based approach for group ranking problems involving different intensities of preference. Stemming from the concept of consensus mining, consensus relationships are discovered by three accumulation matrices and consensus thresholds. An optimization model incorporating the consensus relationships and the concept of Borda majority count is then developed to derive a total ranking list. Compared to previous studies, the proposed approach can treat group ranking problems involving different intensities of preference, reduce the occurrence of ties, and achieve a total ranking list reflecting the consensus preference of the majority of users.