Approximation reduction in inconsistent incomplete decision tables

被引:125
作者
Qian, Yuhua
Liang, Jiye [1 ]
Li, Deyu
Wang, Feng
Ma, Nannan
机构
[1] Informat Proc Minist Educ, Key Lab Computat Intelligence & Chinese, Taiyuan 030006, Peoples R China
基金
中国国家自然科学基金; 国家高技术研究发展计划(863计划);
关键词
Rough set theory; Inconsistent incomplete decision table; Maximal consistent block; Discernibility function; Approximation reduction; KNOWLEDGE REDUCTION; ATTRIBUTE REDUCTION; ROUGH; PERFORMANCE; GRANULATION; ENTROPY; RULES; MODEL;
D O I
10.1016/j.knosys.2010.02.004
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This article deals with approaches to attribute reductions in inconsistent incomplete decision table. The main objective of this study is to extend a kind of attribute reductions called a lower approximation reduct and an upper approximation reduct, which preserve the lower/upper approximation distribution of a target decision. Several judgement theorems of a lower/upper approximation consistent set in inconsistent incomplete decision table are educed. Then, the discernibility matrices associated with the two approximation reductions are examined as well, from which we can obtain approaches to attribute reduction of an incomplete decision table in rough set theory. (C) 2010 Elsevier B.V. All rights reserved.
引用
收藏
页码:427 / 433
页数:7
相关论文
共 38 条
[1]  
[Anonymous], 1996, PROC IPMU
[2]  
[Anonymous], 1992, Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory, DOI DOI 10.1007/978-94-015-7975-9_21
[3]   Reducts within the variable precision rough sets model: A further investigation [J].
Beynon, M .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2001, 134 (03) :592-605
[4]   Set-valued information systems [J].
Guan, Yan-Yong ;
Wang, Hong-Kai .
INFORMATION SCIENCES, 2006, 176 (17) :2507-2525
[5]   Information-preserving hybrid data reduction based on fuzzy-rough techniques [J].
Hu, QH ;
Yu, DR ;
Xie, ZX .
PATTERN RECOGNITION LETTERS, 2006, 27 (05) :414-423
[6]   Mixed feature selection based on granulation and approximation [J].
Hu, Qinghua ;
Liu, Jinfu ;
Yu, Daren .
KNOWLEDGE-BASED SYSTEMS, 2008, 21 (04) :294-304
[7]   Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation [J].
Hu, Qinghua ;
Xie, Zongxia ;
Yu, Daren .
PATTERN RECOGNITION, 2007, 40 (12) :3509-3521
[8]  
Komorowski J., 1999, Rough Fuzzy Hybridization: A New Trend in Decision Making, P3
[9]   Rules in incomplete information systems [J].
Kryszkiewicz, M .
INFORMATION SCIENCES, 1999, 113 (3-4) :271-292
[10]  
Kryszkiewicz M, 2001, INT J INTELL SYST, V16, P105, DOI 10.1002/1098-111X(200101)16:1<105::AID-INT8>3.0.CO