Dominance-based rough set approach to incomplete interval-valued information system

被引：153

作者：

Yang, Xibei ^{[1
,2
]}

Yu, Dongjun ^{[1
]}

Yang, Jingyu ^{[1
]}

Wei, Lihua ^{[1
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Comp Sci & Technol, Nanjing 210094, Jiangsu, Peoples R China

[2] San Jose State Univ, Dept Comp Sci, San Jose, CA 95192 USA

来源：

DATA & KNOWLEDGE ENGINEERING | 2009年 / 68卷 / 11期

关键词：

Decision rule; Dominance-based rough set; Dominance relation; Incomplete interval-valued information system; Knowledge reduction; Relative reduct; GROUP DECISION-MAKING; MULTICRITERIA CLASSIFICATION; MISSING VALUES; RULES; MODEL; PERFORMANCE; REDUCTS;

D O I：

10.1016/j.datak.2009.07.007

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Since preference order is a crucial feature of data concerning decision situations, the classical rough set model has been generalized by replacing the indiscernibility relation with a dominance relation. The purpose of this paper is to further investigate the dominance-based rough set in incomplete interval-valued information system, which contains both incomplete and imprecise evaluations of objects. By considering three types of unknown values in the incomplete interval-valued information system, a data complement method is used to transform the incomplete interval-valued information system into a traditional one. To generate the optimal decision rules from the incomplete interval-valued decision system, six types of relative reducts are proposed. Not only the relationships between these reducts but also the practical approaches to compute these reducts are then investigated. Some numerical examples are employed to substantiate the conceptual arguments. (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：1331 / 1347

页数：17

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