A DES-based group decision model for group decision making with large-scale alternatives

To solve group decision making problems with large-scale alternatives, this paper proposes a dynamic ensemble selection (DES) based group decision model by using historical decision data. The historical decision data of a group of experts are collected from the same multi-criteria decision framework and are mixed to train a set of base classifiers (BCs) to learn group preferences. For each new alternative, the predictions derived from BCs are used to determine its similar historical alternatives from historical data, and the BC with the highest accuracy in predicting the similar historical alternatives is identified as the best individual BC for the new alternative. By iteratively comparing the accuracy of an ensemble of randomly selected BCs and the best individual BC in predicting the similar historical alternatives of the new alternative, a novel DES method is developed to select a competent subset of BCs for the new alternative. The developed DES method effectively avoids the error-independence assumption to a certain extent. Based on the similar historical alternatives determined by the ensemble of selected BCs, a group decision optimization model is developed to learn criterion weights from their assessments on criteria and ensemble predictions derived from the selected BCs. With the learned criterion weights, the understandable group decision result is generated for the new alternative. Case study validates the superiority of the proposed model in diagnosing thyroid nodules using group capabilities. Empirical comparisons on thirty real datasets examine the competence of the proposed DES method against five representative DES methods.