Data aggregation in multi-source assessment model based on evidence theory

• Published in: Applied soft computing 2018-08, Vol.69, p.443-452
• Main Authors: Titkanloo, Hossein Nahid, Keramati, Abbas, Fekri, Roxana
• Format: Article
• Language: English
• Subjects: Data aggregation; Evidence theory; Multi-source assessment; Uncertainty
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2018.05.001

•Looking at employee performance evaluation process from evidence theory point of view.•Improving the accuracy of the results in Multi-Source Assessment model.•Investigating various methods of converting assessment data into belief mass functions.•Investigating the effect of applying different evidence combination rules in the problem.•Investigating the effect of applying different Discounting values to eliminate the conflict. Multisource assessment (MSA) is a common employee performance evaluation model in which subjective views of different groups of assessors about individuals are collected and aggregated. Since in the MSA model, uncertain and subjective assessments are usually aggregated using the Averaging method, the quality of its outputs has been questioned in many studies. Regarding the importance of the data aggregation problem and lack of a proper solution in this context, in this paper, a new model based on the Evidence Theory has been proposed to improve data aggregation process in MSA model. To determine the best implementation mode, the performance of the proposed model has been investigated in various defined conditions through a simulation study. The findings of the paper reveal that, in comparison with the traditional aggregation method, the proposed model significantly increases the accuracy of the outputs in the MSA model.