OWA-based fuzzy rule interpolation for group decision making

The goal in group decision making is to ensure that the best decision is made with respect to the available information and knowledge possessed by all group members. However, different types of uncertainty may influence both the assessment of the individual views and the derivation of the overall group-level solution. The difficulty in such decision-making may escalate if the views of all individuals only cover part of the problem space. Systems capable of reasoning through fuzzy interpolation can help. Fuzzy rule interpolation is an important technique for performing inference with sparse rule bases. Even when a given observation has no overlap with the antecedent values of any existing rules, fuzzy rule interpolation may still derive a conclusion. This paper presents an approach for achieving group decision making via fuzzy interpolation. Individual preferences are firstly aggregated by means of a method learned on rough-fuzzy set theory, and rough-fuzzy interpolation is then applied to derive the group-level conclusion. Experimental investigations are carried out and the results are presented to demonstrate the efficacy of the proposed work in guaranteeing the overall decision accuracy.