{"title":"弱概率语言术语集信息系统中基于 COPRAS 的三向决策方法","authors":"Hai-Long Yang, Xu Liu, Zhi-Lian Guo","doi":"10.1007/s13042-024-02333-x","DOIUrl":null,"url":null,"abstract":"<p>With the development and progress of technology, information becomes increasingly diverse, which poses higher demands on decision-making methods. Probabilistic linguistic term set (PLTS) is a tool that can more intuitively express the evaluations of decision makers (DMs). As a specialized form of PLTS with ignored probabilities, weak probabilistic linguistic term set (WPLTS) can describe incomplete or inaccurate evaluation information. Three-way decision (3WD) is an efficient decision-making method that reduces decision cost by adopting delayed decisions on the boundary domain. In this paper, we propose a novel 3WD method by combining 3WD with the complex proportional assessment (COPRAS) method under the WPLTS environment, named the WPLTS-3WD method. Firstly, we introduce the notion of the WPLTS information system. For a WPLTS information system, we propose a method of complementing the ignored probabilities and a new score function. Secondly, the objects are ranked by the COPRAS method. According to the ranking result, we define the dominance relation and dominance sets. Based on the dominance sets, the conditional probabilities can be estimated. By combining the conditional probabilities with relative loss functions, the expected losses will be obtained and the objects can be classified. Moreover, we propose two conversion functions that can convert real-valued and linguistic term evaluation information into PLTS evaluation information. Finally, we use the proposed WPLTS-3WD method to analyze the air quality of four cities. The rationality and advantages of our method are verified through experimental comparisons with other methods and parameter analysis.</p>","PeriodicalId":51327,"journal":{"name":"International Journal of Machine Learning and Cybernetics","volume":"23 1","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A three-way decision method based on COPRAS in the weak probabilistic linguistic term set information systems\",\"authors\":\"Hai-Long Yang, Xu Liu, Zhi-Lian Guo\",\"doi\":\"10.1007/s13042-024-02333-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>With the development and progress of technology, information becomes increasingly diverse, which poses higher demands on decision-making methods. Probabilistic linguistic term set (PLTS) is a tool that can more intuitively express the evaluations of decision makers (DMs). As a specialized form of PLTS with ignored probabilities, weak probabilistic linguistic term set (WPLTS) can describe incomplete or inaccurate evaluation information. Three-way decision (3WD) is an efficient decision-making method that reduces decision cost by adopting delayed decisions on the boundary domain. In this paper, we propose a novel 3WD method by combining 3WD with the complex proportional assessment (COPRAS) method under the WPLTS environment, named the WPLTS-3WD method. Firstly, we introduce the notion of the WPLTS information system. For a WPLTS information system, we propose a method of complementing the ignored probabilities and a new score function. Secondly, the objects are ranked by the COPRAS method. According to the ranking result, we define the dominance relation and dominance sets. Based on the dominance sets, the conditional probabilities can be estimated. By combining the conditional probabilities with relative loss functions, the expected losses will be obtained and the objects can be classified. Moreover, we propose two conversion functions that can convert real-valued and linguistic term evaluation information into PLTS evaluation information. Finally, we use the proposed WPLTS-3WD method to analyze the air quality of four cities. The rationality and advantages of our method are verified through experimental comparisons with other methods and parameter analysis.</p>\",\"PeriodicalId\":51327,\"journal\":{\"name\":\"International Journal of Machine Learning and Cybernetics\",\"volume\":\"23 1\",\"pages\":\"\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Machine Learning and Cybernetics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s13042-024-02333-x\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Machine Learning and Cybernetics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s13042-024-02333-x","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
A three-way decision method based on COPRAS in the weak probabilistic linguistic term set information systems
With the development and progress of technology, information becomes increasingly diverse, which poses higher demands on decision-making methods. Probabilistic linguistic term set (PLTS) is a tool that can more intuitively express the evaluations of decision makers (DMs). As a specialized form of PLTS with ignored probabilities, weak probabilistic linguistic term set (WPLTS) can describe incomplete or inaccurate evaluation information. Three-way decision (3WD) is an efficient decision-making method that reduces decision cost by adopting delayed decisions on the boundary domain. In this paper, we propose a novel 3WD method by combining 3WD with the complex proportional assessment (COPRAS) method under the WPLTS environment, named the WPLTS-3WD method. Firstly, we introduce the notion of the WPLTS information system. For a WPLTS information system, we propose a method of complementing the ignored probabilities and a new score function. Secondly, the objects are ranked by the COPRAS method. According to the ranking result, we define the dominance relation and dominance sets. Based on the dominance sets, the conditional probabilities can be estimated. By combining the conditional probabilities with relative loss functions, the expected losses will be obtained and the objects can be classified. Moreover, we propose two conversion functions that can convert real-valued and linguistic term evaluation information into PLTS evaluation information. Finally, we use the proposed WPLTS-3WD method to analyze the air quality of four cities. The rationality and advantages of our method are verified through experimental comparisons with other methods and parameter analysis.
期刊介绍:
Cybernetics is concerned with describing complex interactions and interrelationships between systems which are omnipresent in our daily life. Machine Learning discovers fundamental functional relationships between variables and ensembles of variables in systems. The merging of the disciplines of Machine Learning and Cybernetics is aimed at the discovery of various forms of interaction between systems through diverse mechanisms of learning from data.
The International Journal of Machine Learning and Cybernetics (IJMLC) focuses on the key research problems emerging at the junction of machine learning and cybernetics and serves as a broad forum for rapid dissemination of the latest advancements in the area. The emphasis of IJMLC is on the hybrid development of machine learning and cybernetics schemes inspired by different contributing disciplines such as engineering, mathematics, cognitive sciences, and applications. New ideas, design alternatives, implementations and case studies pertaining to all the aspects of machine learning and cybernetics fall within the scope of the IJMLC.
Key research areas to be covered by the journal include:
Machine Learning for modeling interactions between systems
Pattern Recognition technology to support discovery of system-environment interaction
Control of system-environment interactions
Biochemical interaction in biological and biologically-inspired systems
Learning for improvement of communication schemes between systems