Rough Set Model for Cognitive Expectation Embedded Interval-Valued Decision Systems

被引:1
作者
Dai Jianhua [1 ,2 ]
Liu Zhenbo [1 ]
Hu Hu [2 ]
Shi Hong [1 ]
机构
[1] Tianjin Univ, Sch Comp Sci & Technol, Tianjin 300350, Peoples R China
[2] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
Rough set; Interval-valued decision system with cognitive expectation; Attribute reduction; FEATURE-SELECTION;
D O I
10.1049/cje.2017.09.024
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Interval-valued information system is a kind of knowledge representation model for uncertain information. An interval-valued attribute has an expectation by experience or background knowledge, called cognitive expectation. There are few studies aiming at interval-valued attributes with cognitive expectations. We propose the concept of Interval-valued decision system with expectations (IDSE). A new dominance relation based on the distances between expectations and interval values is constructed, Based on the constructed dominance relation, a rough set model for IDSE is investigated. Attribute reduction in IDSE is also examined by using discernibility matrices and discernibility functions.
引用
收藏
页码:675 / 679
页数:5
相关论文
共 50 条
  • [31] Interval-valued fuzzy discernibility pair approach for attribute reduction in incomplete interval-valued information systems
    Dai, Jianhua
    Wang, Zhiyang
    Huang, Weiyi
    INFORMATION SCIENCES, 2023, 642
  • [32] Systematic studies on three-way decisions with interval-valued decision-theoretic rough sets
    Liang, Decui
    Liu, Dun
    INFORMATION SCIENCES, 2014, 276 : 186 - 203
  • [33] Adaptive weighted generalized multi-granulation interval-valued decision-theoretic rough sets
    Guo, Yanting
    Tsang, Eric C. C.
    Xu, Weihua
    Chen, Degang
    KNOWLEDGE-BASED SYSTEMS, 2020, 187
  • [34] Relative Reduction of Incomplete Interval-valued Decision Information Systems Associated with Evidence Theory
    Lin, Bingyan
    Zhang, Xiaoyan
    JOURNAL OF INFORMATION SCIENCE AND ENGINEERING, 2019, 35 (06) : 1377 - 1396
  • [35] Positive approximation and converse approximation in interval-valued fuzzy rough sets
    Cheng, Yi
    Miao, Duoqian
    Feng, Qinrong
    INFORMATION SCIENCES, 2011, 181 (11) : 2086 - 2110
  • [36] Fusing multiple interval-valued fuzzy monotonic decision trees
    Chen, Jiankai
    Li, Zhongyan
    Wang, Xin
    Su, Han
    Zhai, Junhai
    INFORMATION SCIENCES, 2024, 676
  • [37] Attribute Reduction in an Incomplete Interval-Valued Decision Information System
    Chen, Yiying
    Li, Zhaowen
    Zhang, Gangqiang
    IEEE ACCESS, 2021, 9 : 64539 - 64557
  • [38] A rough set model for incomplete and multi-valued information systems
    Qiu, Taorong
    Liu, Lu
    Duan, Longzhen
    Zhou, Shilin
    Huang, Haiquan
    International Journal of Digital Content Technology and its Applications, 2012, 6 (20) : 53 - 61
  • [39] Three-level and three-way uncertainty measurements for interval-valued decision systems
    Liao, Shengjun
    Zhang, Xianyong
    Mo, Zhiwen
    INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2021, 12 (05) : 1459 - 1481
  • [40] Feature selection for interval-valued data via FRIC-model
    Hu, Chunjiao
    Huang, Hengjie
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2023, 44 (01) : 919 - 938