A linguistic belief-based evidential reasoning approach and its application in aiding lung cancer diagnosis

Abstract

Evidential Reasoning (ER) approach is a widely used information aggregation method to deal with uncertain information in decision making. However, as decision-making problem becomes complicated, it is usually difficult for experts to provide accurate belief degrees for each evaluation grade. In this regard, the linguistic belief structure allows experts to give belief degrees with linguistic terms. In this study, we extend the classical ER approach to the linguistic belief-based ER (LB-ER) approach in which the hesitancy degrees are introduced to determine the weights of experts. Afterwards, the LB-ER approach is further enhanced to deal with multi-expert multi-criteria decision-making (MEMCDM) problems, where the best worst method (BWM) is introduced to generate the weights of criteria. Finally, to verify the practicability of the proposed method, we implement the method in lung cancer diagnosis.