Rule acquisition and complexity reduction in formal decision contexts

被引：88
|
作者：
Shao, Ming-Wen [1 ]
Leung, Yee [2 ]
Wu, Wei-Zhi [3 ]
机构：
[1] Qingdao Technol Univ, Comp Engn Inst, Qingdao 266520, Shandong, Peoples R China
[2] Chinese Univ Hong Kong, Inst Environm Energy & Sustainabil, Dept Geog & Resource Management, Hong Kong, Hong Kong, Peoples R China
[3] Zhejiang Ocean Univ, Sch Math Phys & Informat Sci, Zhoushan 316004, Zhejiang, Peoples R China
来源：
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING | 2014年 / 55卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Attribute reduction; Complexity reduction; Formal concept analysis; Rough set; Rules acquisition; THEORETIC ROUGH SET; ATTRIBUTE REDUCTION; KNOWLEDGE REDUCTION; INFORMATION-SYSTEMS; LATTICES; PRECISION; VIEW;
D O I：
10.1016/j.ijar.2013.04.011
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we introduce the notion of formal decision context as an extension of formal contexts by employing the notion of decision information table. We use formal concept analysis to formulate an approach to extract "if-then" rule from formal decision contexts. We also construct a knowledge-lossless method for complexity reduction in formal decision contexts so that the maximum rules extracted from the reduced formal decision contexts are identical to that extracted from the initial decision formal contexts. More specifically, we develop the discernibility matrix and the discernibility function in formal decision contexts to compute all of the attribute reductions without loss of knowledge. (C) 2013 Elsevier Inc. All rights reserved.
引用
收藏
页码：259 / 274
页数：16
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