Probabilistic graded rough set and double relative quantitative decision-theoretic rough set

被引：15
|
作者：
Fang, Bo Wen [1 ]
Hu, Bao Qing [1 ,2 ]
机构：
[1] Wuhan Univ, Sch Math & Stat, Wuhan 430072, Peoples R China
[2] Wuhan Univ, Computat Sci Hubei Key Lab, Wuhan 430072, Peoples R China
来源：
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING | 2016年 / 74卷
基金：
中国国家自然科学基金;
关键词：
Probabilistic graded rough set; Probabilistic rough set; Graded rough set; Absolute quantitative information; Relative quantitative information; APPROXIMATION OPERATORS; FUZZY; MODEL; SYSTEMS;
D O I：
10.1016/j.ijar.2016.03.004
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The probabilistic rough set (PRS) model ignores absolute quantitative information i.e., overlap between equivalence class and basic set. And graded rough set (GRS) model cannot reflect the distinctive degrees of information. In order to overcome these defects, this paper proposes the probabilistic graded rough set (PGRS), which is an extension of Pawlak's rough set and GRS. What is more, we propose double relative quantitative decision theoretic rough set (Drq-DTRS) models, which essentially indicate the relative and absolute quantification. (C) 2016 Elsevier Inc. All rights reserved.
引用
收藏
页码：1 / 12
页数：12
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