On Multigranular Approximate Rough Equivalence of Sets and Approximate Reasoning

被引:2
|
作者
Tripathy, B. K. [1 ]
Saraf, Prateek [1 ]
Parida, S. Ch. [2 ]
机构
[1] VIT Univ, Sch Comp Sci & Engn, Vellore 632014, Tamil Nadu, India
[2] KBV Mahavidyalaya, Dept Math, Ganjam 761104, Odisha, India
关键词
Rough sets; Approximate equalities; Approximate equivalence; Optimistic multigranulation; Pessimistic multigranulation; Replacement properties; EQUALITIES;
D O I
10.1007/978-81-322-2208-8_55
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
As the notion of equality in mathematics is too stringent and less applicable in real life situations, Novotny and Pawlak introduced approximate equalities through rough sets. Three more types of such equalities were introduced by Tripathy et al. as further generalisations of these equalities. As rough set introduced by Pawlak is unigranular from the granular computing point of view, two types of multigranulations rough sets called the optimistic and the pessimistic multigranular rough sets have been introduced. Three of the above approximate equalities were extended to the multigranular context by Tripathy et al. recently. In this paper, we extend the last but the most general of these approximate equalities to the multigranular context. We establish several direct and replacement properties of this type of approximate equalities. Also, we illustrate the properties as well as provide counter examples by taking a real life example.
引用
收藏
页码:605 / 616
页数:12
相关论文
共 50 条
  • [21] Approximate reasoning in knowledge-based fuzzy sets
    Intan, R
    Mukaidono, M
    [J]. 2002 ANNUAL MEETING OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY PROCEEDINGS, 2002, : 439 - 444
  • [22] LT-FUZZY SETS AND LOGICS FOR APPROXIMATE REASONING
    RASIOWA, H
    [J]. FUZZY SETS AND SYSTEMS, 1992, 52 (03) : 354 - 355
  • [23] Approximate reasoning in MAS: Rough set approach extended abstract
    Skowron, Andrzej
    [J]. 2006 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE, (WI 2006 MAIN CONFERENCE PROCEEDINGS), 2006, : 12 - 18
  • [24] Approximate reasoning in MAS: Rough set approach - Extended abstract
    Skowran, Andrzej
    [J]. 2006 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON INTELLIGENT AGENT TECHNOLOGY, PROCEEDINGS, 2006, : 12 - 18
  • [25] On Covering based Pessimistic Multigranular Rough Sets
    Tripathy, B. K.
    Govindarajulu, K.
    [J]. 2014 6TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMMUNICATION NETWORKS, 2014, : 708 - 713
  • [26] Mining Approximate Descriptions Using Rough Sets and Genetic Algorithms
    Mimaroglu, Selim
    [J]. JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING, 2013, 20 (3-4) : 309 - 334
  • [27] Approximate reduct computation by rough sets based attribute weighting
    Al-Radaideh, QA
    Sulaiman, MN
    Selamat, MH
    Ibrahim, H
    [J]. 2005 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING, VOLS 1 AND 2, 2005, : 383 - 386
  • [28] Φ-Rough Sets Theory and Its Usage on Mining Approximate Dependencies
    Xiao, Yiyong
    Kaku, Ikou
    Chang, Wenbing
    [J]. ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, PROCEEDINGS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE, 2008, 5227 : 922 - +
  • [29] An Analysis of Generalised Approximate Equalities Based on Rough Fuzzy Sets
    Tripathy, B. K.
    Jhawar, Abhishek
    Vats, Ekta
    [J]. PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON SOFT COMPUTING FOR PROBLEM SOLVING (SOCPROS 2011), VOL 1, 2012, 130 : 333 - 346
  • [30] Approximate Value Equivalence
    Grimm, Christopher
    Barreto, Andre
    Singh, Satinder
    [J]. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 35, NEURIPS 2022, 2022,