A Study on Group Decision Making Problem Based on Fuzzy Reasoning and Bayesian Networks
This addresses group decision-making problems in domains like student evaluation, though it appears incremental as it combines existing techniques (fuzzy reasoning and Bayesian networks).
The study tackled group decision-making with multi-objective attributes by proposing a system integrating fuzzy inference and Bayesian networks, achieving 86.0% classification accuracy and a 53.4% F1 improvement over traditional methods in a student evaluation case.
Aiming at the group decision - making problem with multi - objective attributes, this study proposes a group decision - making system that integrates fuzzy inference and Bayesian network. A fuzzy rule base is constructed by combining threshold values, membership functions, expert experience, and domain knowledge to address quantitative challenges such as scale differences and expert linguistic variables. A hierarchical Bayesian network is designed, featuring a directed acyclic graph with nodes selected by experts, and maximum likelihood estimation is used to dynamically optimize the conditional probability table, modeling the nonlinear correlations among multidimensional indices for posterior probability aggregation. In a comprehensive student evaluation case, this method is compared with the traditional weighted scoring approach. The results indicate that the proposed method demonstrates effectiveness in both rule criterion construction and ranking consistency, with a classification accuracy of 86.0% and an F1 value improvement of 53.4% over the traditional method. Additionally, computational experiments on real - world datasets across various group decision scenarios assess the method's performance and robustness, providing evidence of its reliability in diverse contexts.