An Outlier Detection Informed Aggregation Approach for Group Decision Making

Chunru Chen, Tianhua Chen, Zhongmin Wang, Yanping Chen, Hengshan Zhang

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In group decision-making, owing to differences that may result from perspectives such as experience and knowledge, the evaluations about the same decision problem provided by different crowd participants may have great differences. Those with huge differences in evaluations from most participants are termed outliers in this chapter. Reaching a decision consensus that satisfies most people is very difficult. In order to solve this problem, many researchers have conducted consensus research. To avoid this problem, this chapter proposes an expert opinions aggregation method based on outlier detection. First, the decision maker evaluates the decision problem based on the Pythagoras Fuzzy Sets(PFSs) from the positive and the negative views. Second, the outliers of expert opinions are detected and then aggregated to obtain the overall decision result. The effectiveness of the proposed method is finally demonstrated using a case study.
Original languageEnglish
Title of host publicationFuzzy Logic
Subtitle of host publicationRecent Applications and Developments
EditorsJenny Carter, Tianhua Chen, Francisco Chiclana Parilla, Arjab Singh Khuman
PublisherSpringer, Cham
Publication statusAccepted/In press - 16 Oct 2020

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