Fuzzy Boost Classifier of Decision Experts for Multicriteria Group Decision-Making

• Published in: Complexity (New York, N.Y.) Vol. 2020; no. 2020; pp. 1 - 10
• Main Authors: Zhao, Zhi-yao, Yu, Jia-bin, Bai, Yuting, Yang, Yi, Wang, Xiao-yi, Xue-bo, Jin
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc; Wiley
• Subjects: Algorithms; Classification; Classifiers; Computational linguistics; Decision analysis; Decision making; Decision support systems; Experts; Feature extraction; Fuzzy sets; Information technology; Language processing; Libraries; Linguistics; Machine learning; Methods; Multiple criterion; Natural language interfaces; Support vector machines
• ISSN: 1076-2787; 1099-0526; 1099-0526
• DOI: 10.1155/2020/8147617

The expert is a vital role in multicriteria decision-making, which provides source decision opinions. In the existing group decision-making activities, the selection of experts is usually conducted artificially, which relies on personal subjective experience. It has been the urgent demand for an automatic selection of experts, which can help to determine their weights for the follow-up decision calculation. In this paper, an expert classification method is proposed to solve the problem. First, the CatBoost classification algorithm is improved by integrating the 2-tuple linguistic, which can effectively extract the features of samples. Second, the framework of the expert classification is designed. The flow combines the expert resume collection, expert classification, and database update. Third, a decision-making case is analyzed for the expert selection issue. The experiment and result indicate that the proposed classifier performs better than the classic methods. The proposed classification method of the decision experts can support the automatic and intelligent operation of the decision-making activities.