JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2014, Vol. 49 ›› Issue (08): 73-79.doi: 10.6040/j.issn.1671-9352.1.2014.089

Previous Articles     Next Articles

Tolerance rough fuzzy set model

ZHAI Jun-hai1,2, ZHANG Yao1, WANG Xi-zhao1,2   

  1. 1. College of Mathematics and Computer Science, Hebei University, Baoding 071002, Hebei, China;
    2. Key Lab. of Machine Learning and Computational Intelligence, Hebei University, Baoding 071002, Hebei, China
  • Received:2014-04-15 Revised:2014-07-01 Published:2014-09-24
  • Supported by:
    翟俊海（1964-），男，博士，教授，研究方向为机器学习与模式识别.E-mail：mczjh@126.com

PDF (PC)

869

Abstract: In rough fuzzy set model, the approached target concept is a fuzzy set, the knowledge used for approaching the target concept is equivalence relation, in other words, the attributes used for representing the object is of discrete value. However, in many practical applications, the attributes used for representing the object is of real value. In order to deal with this problem, the equivalence relation in rough fuzzy set model is extended to tolerance relation; the tolerance rough fuzzy set model is proposed. When the tolerance relation is degenerated into equivalence relation, the tolerance rough fuzzy set model becomes the rough fuzzy set model; tolerance rough fuzzy set model extends the application of rough fuzzy set.

Key words: rough set, fuzzy set, tolerance rough set, tolerance rough fuzzy set, rough fuzzy set

CLC Number: 

  • TP181
[1] PAWLAK Z. Rough sets[J]. International Journal of Information and Computer Sciences, 1982, 11(5):341-356.
[2] ZHAI Junhai, GAO Yuanyuan, ZHAI Mengyao, et al. Rough set model and its eight extensions[C]//2011 IEEE International Conference on Systems Man and Cybernetics. Anchorage, Alaska, USA:[s.n.], 2011: 3512-3517.
[3] ZIARKO W. Variable precision rough set model[J]. Journal of Computer and System Sciences, 1993, 46:39-59.
[4] WONG S K W, ZIARKO W. Comparison of the probabilistic approximate classification and the fuzzy set model[J]. Fuzzy Sets and Systems, 1987, 21(3):357-362.
[5] YAO Yi-yu, WONG S K M. A decision theoretic framework for approximating concepts[J]. International Journal of Man-machine Studies, 1992, 37:793-809.
[6] YAO Yiyu. Generalized rough set models[M]//Polkowski L, Skowron A. Rough Sets in Knowledge Discovery, Heidelberg: Physica-Verlag, 1998: 286-318.
[7] SKOWRON A. Tolerance approximation spaces[J]. Fundamenta Informaticae, 1996, 27(2-3):245-253.
[8] GRECO S, MATARAZZO B, SLOWINSKI R. Rough Approximation of a preference relation by dominance relations[J]. European Journal of Operational Research, 1999, 117:63-83.
[9] DUBOIS D, PRADE H. Rough fuzzy sets and fuzzy rough sets[J]. International Journal of General Systems, 1990, 17:191-208.
[10] ALICJA M R, LESZEK R. Variable precision fuzzy rough sets[J]. Transactions on Rough Sets I, Lecture Notes in Computer Science, 2004, 3100:144-160.
[11] S＇LEZAK D, ZIARKO W. Variable Precision Bayesian Rough Set Model. Rough Sets[M] Fuzzy Sets, Data Mining, and Granular Computing, Lecture Notes in Computer Science, Berlin, Heidelberg: Springer-Verlag, 2003, 2639:312-315.
[12] 翟俊海,翟梦尧,高原原,等. 变精度相容粗糙集模型[J]. 计算机工程与应用, 2012, 48(26):134-138. ZHAI Junhai, ZHAI Mengyao, GAO Yuanyuan, et al. Model of variable precision tolerance rough set[J]. Computer Engineering and Applications, 2012, 48(26):134-138.
[13] ZADEH L A. Fuzzy sets[J]. Information and Control, 1965, 8:338-353.
[14] YAO Yiyu. Combination of rough and fuzzy sets based on α-level sets[M]//LIN Tong-yan, Cercone N. Rough Sets and Data Mining-Analysis of Imprecise Data, Heidelberg: Physica-Verlag, 1997: 301-321.
[1] LI Tong-jun, HUANG Jia-wen, WU Wei-zhi. Attribute reduction of incomplete contexts based on similarity relations [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(8): 9-16.
[2] ZUO Zhi-cui, ZHANG Xian-yong, MO Zhi-wen, FENG Lin. Block discernibility matrix based on decision classification and its algorithm finding the core [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(8): 25-33.
[3] LI Li, GUAN Tao, LIN He. The hybrid parallel rough set model based on pansystems operators [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(7): 22-29.
[4] HU Qian, MI Ju-sheng, LI Lei-jun. The fuzzy belief structure and attribute reduction based on multi-granulation fuzzy rough operators [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(7): 30-36.
[5] WANG Xia, ZHANG Qian, LI Jun-yu, LIU Qing-feng. Triadic concept analysis based on rough set theory [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(7): 37-43.
[6] WANG Xiao-yan, SHEN Jia-lan, SHEN Yuan-xia. Graded multi-granulation rough set based on weighting granulations and dominance relation [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(3): 97-104.
[7] HUANG Wei-ting, ZHAO Hong, ZHU William. Adaptive divide and conquer algorithm for cost-sensitive attribute reduction [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(8): 98-104.
[8] PENG Jia-yin. Soft filters of pseudo-BL algebras related to fuzzy set theory [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2015, 50(08): 40-45.
[9] LI Ling-qiang, LI Qing-guo. The characterizations of lattice-valued fuzzy lower approximation operators by a unique axiom [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(10): 78-82.
[10] AN Qiu-sheng, KONG Xiang-yu. New research of functional dependency and multi-valued dependency [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(08): 1-5.
[11] WU Zheng-jiang, LIU Yong-li, GAO Yan. Cover rough sets on a semi-monolayer cover [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(08): 6-14.
[12] LIN Zi-qiong, WANG Jing-qian, ZHU William. Computing minimal description and maximal description in covering-based rough sets through matrices [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(08): 97-101.
[13] SHI Su-wei, LI Jin-jin, TAN An-hui. Fuzzy roughness and rough entropy of covering based generalized rough intuitionistic fuzzy set model [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(08): 86-91.
[14] LUO hai-yan, LÜ Ping, LIU Lin-zhong, YANG Xun. Enterprises trust comprehensive evaluation based on fussy rough AHP in cloud computing [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(08): 111-117.
[15] XU Feng-sheng1, YU Xiu-qing1, ZHANG Huan-li2. S-rough equivalent classes and knowledge dynamic miningdiscovery [J]. J4, 2013, 48(3): 37-41.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright 2018 © Editorial office of JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE)
Supported by Beijing Magtech Co.Ltd