Multi-expert decision making using rough-fuzzy rule interpolation

The goal in multi-expert decision making is to ensure that the best decision is made with respect to the available information and knowledge possessed by all experts. 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 multi-expert decision making may escalate if the views of all individuals only cover part of the problem space. Systems capable of reasoning through fuzzy rule 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 multi-expert decision making via fuzzy rule interpolation. Individual preferences are firstly aggregated by means of a method learned on rough-fuzzy set theory, and rough-fuzzy rule interpolation is then applied to derive the group-level conclusion. An application to real datasets is carried out and the results are presented to demonstrate the efficacy of the proposed work in guaranteeing the overall decision accuracy.