Managing classification-based consensus in social network group decision making: An optimization-based approach with minimum information loss

This study proposes a classification-based consensus framework in social network group decision making, which aims to classify alternatives into several ordinal classes from best to worst. In the classification-based consensus framework, a maximum consensus-based optimization model is devised to determine the weight of decision makers by linearly combining three reliable sources: in-degree centrality, consistency and similarity indexes. This is done by maximizing the consensus level among decision makers regarding the collective classification of alternatives. Following this, a minimum information loss-based optimization model is constructed to generate the consensual collective classification of alternatives. It seeks to minimize the information loss between the additive preference relations provided by decision makers and their preference vectors. Particularly, the proposed optimization models are converted into 0-1 mixed linear programming models to easily find their optimal solutions. Finally, a numerical example and a detailed comparison analysis are provided to show the effectiveness of the proposed approach.