Uncertainty measurement for incomplete interval-valued information systems based on α-weak similarity

被引：72
作者：
Dai, Jianhua [1 ,2 ]
Wei, Bingjie [2 ]
Zhang, Xiaohong [3 ]
Zhang, Qilai [2 ]
机构：
[1] Hunan Normal Univ, Coll Math & Comp Sci, Key Lab High Performance Comp & Stochast Informat, Minist Educ China, Changsha 410081, Hunan, Peoples R China
[2] Tianjin Univ, Sch Comp Sci & Technol, Tianjin 300350, Peoples R China
[3] Shaanxi Univ Sci & Technol, Sch Arts & Sci, Xian 710021, Shaanxi, Peoples R China
来源：
KNOWLEDGE-BASED SYSTEMS | 2017年 / 136卷
基金：
中国国家自然科学基金;
关键词：
Incomplete interval-valued information; Rough sets; Uncertainty measure; Weak similarity; ROUGH SET APPROACH; CLASSIFICATION; RULES;
D O I：
10.1016/j.knosys.2017.09.009
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Rough set theory is a powerful mathematical tool to deal with uncertainty in data analysis. Interval valued information systems are generalized models of single-valued information systems. Recently, uncertainty measures for complete interval-valued information systems or complete interval-valued decision systems have been developed. However, there are few studies on uncertainty measurements for incomplete interval-valued information systems. This paper aims to investigate the uncertainty measures in incomplete interval-valued information systems based on an alpha-weak similarity. Firstly, the maximum and the minimum similarity degrees are defined when interval-values information systems are incomplete based on the similarity relation. The concept of alpha-weak similarity relation is also defined. Secondly, the rough set model is constructed. Based on this model, accuracy, roughness and approximation accuracy are given to evaluate the uncertainty in incomplete interval-valued information systems. Furthermore, experimental analysis shows the effectiveness of the constructed uncertainty measures for incomplete interval-valued information systems. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码：159 / 171
页数：13
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