General type-2 fuzzy rough sets based on α-plane Representation theory

被引:0
作者
Zhao, Tao [1 ]
Xiao, Jian [2 ]
机构
[1] Southwest Jiaotong Univ, Sch Transportat & Logist, Chengdu 610031, Peoples R China
[2] Southwest Jiaotong Univ, Sch Elect Engn, Chengdu 610031, Peoples R China
基金
中国国家自然科学基金;
关键词
General type-2 fuzzy sets; Rough sets; Approximation operators; General type-2 fuzzy relations; alpha-Plane; ATTRIBUTE REDUCTION; LOGIC;
D O I
10.1007/s00500-013-1082-y
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Rough sets theory and fuzzy sets theory are mathematical tools to deal with uncertainty, imprecision in data analysis. Traditional rough set theory is restricted to crisp environments. Since theories of fuzzy sets and rough sets are distinct and complementary on dealing with uncertainty, the concept of fuzzy rough sets has been proposed. Type-2 fuzzy set provides additional degree of freedom, which makes it possible to directly handle highly uncertainties. Some researchers proposed interval type-2 fuzzy rough sets by combining interval type-2 fuzzy sets and rough sets. However, there are no reports about combining general type-2 fuzzy sets and rough sets. In addition, the -plane representation method of general type-2 fuzzy sets has been extensively studied, and can reduce the computational workload. Motivated by the aforementioned accomplishments, in this paper, from the viewpoint of constructive approach, we first present definitions of upper and lower approximation operators of general type-2 fuzzy sets by using -plane representation theory and study some basic properties of them. Furthermore, the connections between special general type-2 fuzzy relations and general type-2 fuzzy rough upper and lower approximation operators are also examined. Finally, in axiomatic approach, various classes of general type-2 fuzzy rough approximation operators are characterized by different sets of axioms.
引用
收藏
页码:227 / 237
页数:11
相关论文
共 27 条
  • [1] On fuzzy-rough sets approach to feature selection
    Bhatt, RB
    Gopal, M
    [J]. PATTERN RECOGNITION LETTERS, 2005, 26 (07) : 965 - 975
  • [2] ROUGH FUZZY-SETS AND FUZZY ROUGH SETS
    DUBOIS, D
    PRADE, H
    [J]. INTERNATIONAL JOURNAL OF GENERAL SYSTEMS, 1990, 17 (2-3) : 191 - 209
  • [3] Fuzzy-rough data reduction with ant colony optimization
    Jensen, R
    Shen, Q
    [J]. FUZZY SETS AND SYSTEMS, 2005, 149 (01) : 5 - 20
  • [4] Fuzzy-rough attribute reduction with application to web categorization
    Jensen, R
    Shen, Q
    [J]. FUZZY SETS AND SYSTEMS, 2004, 141 (03) : 469 - 485
  • [5] Are More Features Better? A Response to Attributes Reduction Using Fuzzy Rough Sets
    Jensen, Richard
    Shen, Qiang
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2009, 17 (06) : 1456 - 1458
  • [6] New Approaches to Fuzzy-Rough Feature Selection
    Jensen, Richard
    Shen, Qiang
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2009, 17 (04) : 824 - 838
  • [7] Type-2 fuzzy logic: A historical view
    John, Robert I.
    Coupland, Simon
    [J]. IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE, 2007, 2 (01) : 57 - 62
  • [8] An efficient centroid type-reduction strategy for general type-2 fuzzy logic system
    Liu, Feilong
    [J]. INFORMATION SCIENCES, 2008, 178 (09) : 2224 - 2236
  • [9] Axiomatic systems for rough sets and fuzzy rough sets
    Liu, Guilong
    [J]. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2008, 48 (03) : 857 - 867
  • [10] α-Plane Representation for Type-2 Fuzzy Sets: Theory and Applications
    Mendel, Jerry M.
    Liu, Feilong
    Zhai, Daoyuan
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2009, 17 (05) : 1189 - 1207