Comparison of reduction in formal decision contexts

被引:77
作者
Li, Jinhai [1 ]
Kumar, Cherukuri Aswani [2 ]
Mei, Changlin [3 ]
Wang, Xizhao [4 ]
机构
[1] Kunming Univ Sci & Technol, Fac Sci, Kunming 650500, Yunnan, Peoples R China
[2] VIT Univ, Sch Informat Technol & Engn, Vellore 632014, Tamil Nadu, India
[3] Xi An Jiao Tong Univ, Sch Math & Stat, Xian 710049, Shaanxi, Peoples R China
[4] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China
基金
中国国家自然科学基金;
关键词
Formal concept analysis; Formal decision context; Rule acquisition; Reduction; Comparison; ATTRIBUTE REDUCTION; ROUGH SET; RULE ACQUISITION; KNOWLEDGE REDUCTION; OBJECT;
D O I
10.1016/j.ijar.2016.08.007
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In formal concept analysis, many reduction methods have recently been proposed for formal decision contexts, and each of them was to reduce formal decision contexts with a particular purpose. However, little attention has been paid to the comparison of their differences from various aspects. In fact, this problem is very important because it can provide evidence to select an appropriate reduction method for a given specific case. To address this problem, our study mainly focuses on clarifying the relationship among the existing reduction methods in formal decision contexts. Firstly, we give a rule-based review of the existing reduction methods, revealing the type of rules that each of them can preserve. Secondly, we analyze the relationship among the consistencies introduced by the existing reduction methods. More specifically, Wei's first consistency (see [39]) is stronger than others, while her second one is weaker than the remainder except Wu's consistency (see [43]). Finally, we make a comparison of the existing reductions, concluding that Li's reduction (see [14]) maintaining the non-redundant decision rules of a formal decision context is coarser than others. The results obtained in this paper are beneficial for users to select an appropriate reduction method for meeting their requirements. (C) 2016 Elsevier Inc. All rights reserved.
引用
收藏
页码:100 / 122
页数:23
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