A three-way decision method based on COPRAS in the weak probabilistic linguistic term set information systems

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Machine Learning and Cybernetics Pub Date : 2024-09-09 DOI:10.1007/s13042-024-02333-x
Hai-Long Yang, Xu Liu, Zhi-Lian Guo
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Abstract

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.

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弱概率语言术语集信息系统中基于 COPRAS 的三向决策方法
随着科技的发展和进步,信息变得越来越多样化,这对决策方法提出了更高的要求。概率语言术语集(PLTS)是一种能更直观地表达决策者(DMs)评价的工具。弱概率语言术语集(WPLTS)是忽略概率的 PLTS 的一种特殊形式,可以描述不完整或不准确的评价信息。三向决策(3WD)是一种高效的决策方法,它通过在边界域采用延迟决策来降低决策成本。本文在 WPLTS 环境下将 3WD 与复杂比例评估(COPRAS)方法相结合,提出了一种新颖的 3WD 方法,命名为 WPLTS-3WD 方法。首先,我们介绍 WPLTS 信息系统的概念。针对 WPLTS 信息系统,我们提出了一种补充忽略概率的方法和一种新的评分函数。其次,采用 COPRAS 方法对对象进行排序。根据排序结果,我们定义了支配关系和支配集。根据支配集,可以估算出条件概率。将条件概率与相对损失函数相结合,就能得到预期损失,从而对对象进行分类。此外,我们还提出了两种转换函数,可将实值和语言术语评价信息转换为 PLTS 评价信息。最后,我们使用所提出的 WPLTS-3WD 方法分析了四个城市的空气质量。通过与其他方法的实验比较和参数分析,验证了我们方法的合理性和优势。
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来源期刊
International Journal of Machine Learning and Cybernetics
International Journal of Machine Learning and Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
7.90
自引率
10.70%
发文量
225
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
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