Definability in Logic and Rough Set Theory

被引：0
|
作者：
Fan, Tuan-Fang [1 ,2 ]
Liau, Churn-Jung [3 ]
Liu, Duen-Ren [2 ]
机构：
[1] Natl Penghu Univ, Dept Comp Sci & Informat Engn, Penghu 880, Taiwan
[2] Natl Chiao Tung Univ, Inst Informat Management, Hsinchu 300, Taiwan
[3] Acad Sinica, Inst Informat Sci, Taipei 115, Taiwan
来源：
ECAI 2008, PROCEEDINGS | 2008年 / 178卷
关键词：
D O I：
10.3233/978-1-58603-891-5-749
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Rough set theory is an effective tool for data mining. According to the theory, a concept is definable if it can be written as a Boolean combination of equivalence classes induced from classification attributes. On the other hand, definability in logic has been explicated by Beth's theorem. In this paper, we propose two data representation formalisms, called first-order data logic (FODL) and attribute value-sorted logic (AVSL), respectively. Based on these logics, we explore the relationship between logical definability and rough set definability.
引用
收藏
页码：749 / +
页数：2
相关论文
共 50 条
  • [41] Stochastic approach to rough set theory
    Ziarko, Wojciech
    [J]. ROUGH SETS AND CURRENT TRENDS IN COMPUTING, PROCEEDINGS, 2006, 4259 : 38 - 48
  • [42] Advances in Rough Set Theory Preface
    Ciucci, Davide
    Yao, Yiyu
    [J]. FUNDAMENTA INFORMATICAE, 2011, 108 (3-4) : I - I
  • [43] Measuring uncertainty in rough set theory
    Wierman, Mark J.
    [J]. International Journal of General Systems, 1999, 28 (04): : 283 - 297
  • [44] Knowledge entropy in rough set theory
    Li, M
    Zhang, XF
    [J]. PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2004, : 1408 - 1412
  • [45] Rough Set Theory in the Classification of Diagnoses
    Rakus-Andersson, Elisabeth
    [J]. COMPUTERS IN MEDICAL ACTIVITY, 2009, 65 : 41 - 51
  • [46] Equivalence relations and rough set theory
    Guan, JW
    Bell, DA
    Liu, DY
    [J]. PROCEEDINGS OF THE 7TH JOINT CONFERENCE ON INFORMATION SCIENCES, 2003, : 104 - 107
  • [47] An Approach of Proximity in Rough Set Theory
    Tiwari, Surabhi
    Singh, Pankaj Kumar
    [J]. FUNDAMENTA INFORMATICAE, 2019, 166 (03) : 251 - 271
  • [48] Granular computing and rough set theory
    Zadeh, Lotfi A.
    [J]. Rough Sets and Intelligent Systems Paradigms, Proceedings, 2007, 4585 : 1 - 4
  • [49] Efficient Rough Set Theory Merging
    Grabowski, Adam
    [J]. FUNDAMENTA INFORMATICAE, 2014, 135 (04) : 371 - 385
  • [50] Discovery through rough set theory
    Ziarko, W
    [J]. COMMUNICATIONS OF THE ACM, 1999, 42 (11) : 54 - 57
12345