Fuzzy Boost Classifier of Decision Experts for Multicriteria Group Decision-Making
| Autor: | Yuting Bai, Xiaoyi Wang, Zhi-yao Zhao, Yi Yang, Jiabin Yu, Xue-Bo Jin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Multidisciplinary General Computer Science Article Subject business.industry Computer science 02 engineering and technology QA75.5-76.95 Machine learning computer.software_genre Fuzzy logic Group decision-making 020901 industrial engineering & automation Electronic computers. Computer science 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Classifier (UML) |
| Zdroj: | Complexity, Vol 2020 (2020) |
| ISSN: | 1076-2787 |
| DOI: | 10.1155/2020/8147617 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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