Three-way decision method based on triangular norms in incomplete information systems and its applications in medical diagnosis

计算机科学 扩展(谓词逻辑) 关系(数据库) 集合(抽象数据类型) 粗集 完整信息 理论(学习稳定性) 过程(计算) 机器学习 数据挖掘 人工智能 数学 数理经济学 操作系统 程序设计语言
作者
Yanlong Tang,Junsheng Qiao
出处
期刊:Applied Soft Computing [Elsevier BV]
卷期号:159: 111657-111657 被引量:14
标识
DOI:10.1016/j.asoc.2024.111657
摘要

Three-way decision, as an extension of traditional two-way decision, was proposed by Yao in 2009, which can effectively avoid unnecessary losses caused by incorrect decisions in the decision-making process. Meanwhile, incomplete hybrid information systems represent the database of the relationship between objects and attributes, which refers to a system with multiple data and missing data. In this paper, based on the fact that many existing literatures involving incomplete hybrid information systems did not fully consider the impact of different conditional attributes on decision attributes and lacked effective aggregation methods to integrate weights and distances, we propose a new three-way decision method to deal with incomplete hybrid information systems with the help of triangular norms. First, in incomplete hybrid information systems, we redefine the distance between two objects based on conditional attributes and give the calculation formula of different data attributes in conditional attributes. At the same time, we define a new weight calculation method based on the relationship between conditional attributes and decision attributes. And then, by the distance between different conditional attributes and their corresponding weights, the hybrid distance is obtained using triangular norms. Furthermore, we use the hybrid distance to get the tolerance relation on the target set of an incomplete hybrid information system, thereby, using this tolerance relation, we get a new decision theoretic rough set model and the corresponding decision rules. Finally, by two experiments involving medical diagnosis, we demonstrate that our model has better classification ability, lower misclassification rate, and better stability compared to other corresponding models, thereby confirming that the proposed model provides a new and effective method for handling incomplete hybrid information systems in practical applications.
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