计算优势关系粗糙集中近似集的快速算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201702007

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (740 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要优势关系粗糙集克服经典粗糙集无法处理偏序关系数据的缺陷,而减少近似集的计算时间可以提高数据处理的效率.基于此种情况,文中提出计算优势关系粗糙集中近似集的快速算法,在对象和属性同时增加时,能快速计算优势关系粗糙集的近似集.算法改进近似集相关参数的定义,通过尽可能少的参数求出近似集,简化计算过程,提高算法运算速度,节省内存.实验表明,文中算法具有较快的运算速度,尤其当数据量增大或数据类别增多时效果更明显.

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	王澍
	李天瑞

关键词 ：粗糙集, 优势关系粗糙集, 近似集, 快速算法

Abstract：The classical rough set can not process preference-ordered data. Dominance-based rough set (DRST) overcomes this drawback. The data processing efficiency can be improved by reducing the time of computing approximations. A fast algorithm for computing approximations is presented. The approximations are acquired quickly while objects and attributes being added simultaneously in DRST. The definitions of parameters related to approximations are revised in the proposed fast algorithm and thus approximations can be calculated by parameters as few as possible. Consequently, the calculation is simplified and accelerated, and the memory consumption is reduced as well. The experimental results demonstrate that the proposed algorithm is faster than other algorithms and it is especially efficient with larger data sizeand data label.

Key words： Rough Set Dominance-Based Rough Set Approximations Fast Algorithm

收稿日期: 2016-05-15

ZTFLH:

TP 18

基金资助:国家自然科学基金项目(No.61573292)资助

作者简介: 王澍,男,1978年生,博士研究生,主要研究方向为粗糙集、粒计算、大数据.E-mail: swang@swjtu.edu.cn.
李天瑞(通讯作者),男,1969年生,博士,教授,主要研究方向为大数据、云计算、粗糙集、粒计算.E-mail:trli@swjtu.edu.cn.

引用本文:

王澍，李天瑞. 计算优势关系粗糙集中近似集的快速算法^*[J]. 模式识别与人工智能, 2017, 30(2): 162-170. WANG Shu, LI Tianrui. Fast Algorithm for Computing Approximations in
Dominance-Based Rough Set. , 2017, 30(2): 162-170.

链接本文:

http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.201702007 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2017/V30/I2/162

[1] PAWLAK Z. Rough Sets. International Journal of Computer and Information Sciences, 1982, 11(5): 341-356.
[2] 张文修,吴伟志,梁吉业,等.粗糙集理论与方法.北京:科学出版社, 2001.
(ZHANG W X, WU W Z, LIANG J Y, et al. Theories and Methodologies of Rough Sets. Beijing, China: Science Press, 2001.)
[3] CHEN H M, LI T R, QIAO S J, et al. A Rough Set Based Dynamic Maintenance Approach for Approximations in Coarsening and Refining Attribute Values. International Journal of Intelligent Systems, 2010, 25(10): 1005-1026.
[4] CHEN H M, LI T R, LUO C, et al. A Rough Set-Based Method for Updating Decision Rules on Attribute Values′ Coarsening and Refining. IEEE Transaction on Knowledge and Data Engineering, 2014, 26(12): 2886-2899.
[5] CHEN H M, LI T R, RUAN D, et al. A Rough-Set-Based Incremental Approach for Updating Approximations under Dynamic Maintenance Environments. IEEE Transaction on Knowledge and Data Enginee-ring, 2013, 25(2): 274-284.
[6] CHEN H M, LI T R, RUAN D. Maintenance of Approximations in Incomplete Ordered Decision Systems While Attribute Values Coar-sening or Refining. Knowledge-Based Systems, 2012, 31: 140-161.
[7] LUO C, LI T R, ZHANG J B, et al. An Improved Parallel Method for Computing Rough Set Approximations // Proc of the 8th International Conference on Intelligent Systems and Knowledge Enginee-ring. Berlin, Germany: Springer, 2014: 25-34.
[8] LUO C, LI T R, CHEN H M. Dynamic Maintenance of Approximations in Set-Valued Ordered Decision Systems under the Attribute Gene-ralization. Information Sciences, 2014, 257: 210-228.
[9] LUO C, LI T R, CHEN H M, et al. Fast Algorithms for Computing Rough Approximations in Set-Valued Decision Systems While Updating Criteria Values. Information Sciences, 2015, 299: 221-242.
[10] LUO C, LI T R, CHEN H M, et al. Incremental Approaches for Updating Approximations in Set-Valued Ordered Information Systems. Knowledge-Based Systems, 2013, 50: 218-233.
[11] ZHANG J B, WONG J S, PAN Y, et al. A Parallel Matrix-Based Method for Computing Approximations in Incomplete Information Systems. IEEE Transaction on Knowledge and Data Engineering, 2015, 27(2): 326-339.
[12] ZHANG J B, LI T R, RUAN D, et al. A Parallel Method for Computing Rough Set Approximations. Information Sciences, 2012, 194: 209-223.
[13] GRECO S, MATARAZZO B, SLOWINSKI R. Rough Approximation by Dominance Relations. International Journal of Intelligent Systems, 2002, 17(2): 153-171.
[14] 李少勇.有序决策系统的知识更新理论及其高效算法.博士学位论文.成都:西南交通大学, 2014.
(LI S Y. Approaches and Algorithms for Efficiently Updating Knowledge in Preference-Ordered Decision Systems. Ph.D Dissertation. Chengdu, China: Southwest Jiaotong University, 2014.)
[15] LI S Y, LI T R, LIU D. Dynamic Maintenance of Approximations in Dominance-Based Rough Set Approach under the Variation of the Object Set. International Journal of Intelligent Systems, 2013, 28(8): 729-751.
[16] LI S Y, LI T R, LIU D. Incremental Updating Approximations in Dominance-Based Rough Sets Approach under the Variation of the Attribute Set. Knowledge-Based Systems, 2013, 40: 17-26.
[17] CHEN H M, LI T R, LUO C, et al. A Decision-Theoretic Rough Set Approach for Dynamic Data Mining. IEEE Transaction on Fuzzy Systems, 2015, 23(6): 1958-1970.

[18] KOTLOWSKI W, DEMBCZYN′SKI K, GRECO S, et al. Stochastic Dominance-Based Rough Set Model for Ordinal Classification. Information Sciences, 2008, 178(21): 4019-4037.