Three-way decision perspectives on class-specific attribute reducts

被引:85
作者
Ma, Xi-Ao [1 ,2 ]
Yao, Yiyu [2 ]
机构
[1] Zhejiang Gongshang Univ, Sch Comp & Informat Engn, Hangzhou 310018, Zhejiang, Peoples R China
[2] Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada
基金
加拿大自然科学与工程研究理事会; 中国国家自然科学基金;
关键词
Class-specific attribute reduct; Pawlak rough set model; Probabilistic rough set model; Three-way decision; ROUGH SETS; ALGEBRA VIEW; FUZZY; ENTROPY; REGION; MODEL;
D O I
10.1016/j.ins.2018.03.049
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In rough set theory, a decision class (i.e., a subset of objects) is approximated by three pair-wise disjoint positive, boundary, and negative regions. The concept of three-way decisions is introduced to provide a new interpretation of the three regions. We construct acceptance, non-commitment, and rejection rules, respectively, from the positive, boundary, and negative regions. The notion of class-specific attribute reducts concerns a minimal set of attributes used in constructing such rules. Existing studies on class-specific attribute reducts only consider the positive region and hence only the acceptance rules. In many situations such as medical diagnosis, we are also interested in negative rules or rule-out rules. This motivates the present study on three-way decision perspectives on class-specific attribute reducts. In addition to positive-region based attribute reducts, we study negative region and positive-and-negative-region based attribute reducts. We investigate relationships among the three types of reducts. Although the three types of reducts are equivalent in consistent decision tables, they are not equivalent in inconsistent decision tables. By extending the framework, we study the three types of class-specific attribute reducts in probabilistic rough set models and their relationships. Finally, we give a general definition of class-specific attribute reducts. (C) 2018 Elsevier Inc. All rights reserved.
引用
收藏
页码:227 / 245
页数:19
相关论文
共 50 条
[1]   Reducts within the variable precision rough sets model: A further investigation [J].
Beynon, M .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2001, 134 (03) :592-605
[2]   Local reduction of decision system with fuzzy rough sets [J].
Chen Degang ;
Zhao Suyun .
FUZZY SETS AND SYSTEMS, 2010, 161 (13) :1871-1883
[3]   Attribute selection based on a new conditional entropy for incomplete decision systems [J].
Dai, Jianhua ;
Wang, Wentao ;
Tian, Haowei ;
Liu, Liang .
KNOWLEDGE-BASED SYSTEMS, 2013, 39 :207-213
[4]   A Multifaceted Analysis of Probabilistic Three-way Decisions [J].
Deng, Xiaofei ;
Yao, Yiyu .
FUNDAMENTA INFORMATICAE, 2014, 132 (03) :291-313
[5]  
Han B, 2002, P AMER CONTR CONF, V1-6, P4577, DOI 10.1109/ACC.2002.1025373
[6]   Game-Theoretic Rough Sets [J].
Herbert, Joseph P. ;
Yao, JingTao .
FUNDAMENTA INFORMATICAE, 2011, 108 (3-4) :267-286
[7]   Mixed feature selection based on granulation and approximation [J].
Hu, Qinghua ;
Liu, Jinfu ;
Yu, Daren .
KNOWLEDGE-BASED SYSTEMS, 2008, 21 (04) :294-304
[8]   Minimum cost attribute reduction in decision-theoretic rough set models [J].
Jia, Xiuyi ;
Liao, Wenhe ;
Tang, Zhenmin ;
Shang, Lin .
INFORMATION SCIENCES, 2013, 219 :151-167
[9]  
Liu D., 2011, INFORM SCI, V181, P173
[10]   Local attribute reductions for decision tables [J].
Liu, Guilong ;
Hua, Zheng ;
Zou, Jiyang .
INFORMATION SCIENCES, 2018, 422 :204-217