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[1]陈曼如,张楠,童向荣,等.集值信息系统的快速正域约简[J].智能系统学报,2019,14(03):471-478.[doi:10.11992/tis.201804059]
 CHEN Manru,ZHANG Nan,TONG Xiangrong,et al.Quick positive region reduction in set-valued information systems[J].CAAI Transactions on Intelligent Systems,2019,14(03):471-478.[doi:10.11992/tis.201804059]
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集值信息系统的快速正域约简(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年03期
页码:
471-478
栏目:
出版日期:
2019-05-05

文章信息/Info

Title:
Quick positive region reduction in set-valued information systems
作者:
陈曼如1 张楠1 童向荣1 岳晓冬2
1. 烟台大学 数据科学与智能技术山东省高校重点实验室, 山东 烟台 264005;
2. 上海大学 计算机工程与科学学院, 上海 200444
Author(s):
CHEN Manru1 ZHANG Nan1 TONG Xiangrong1 YUE Xiaodong2
1. Key Lab for Data Science and Intelligence Technology of Shandong Higher Education Institutes, Yantai University, Yantai 264005, China;
2. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
关键词:
属性约简粗糙集集值信息系统特征选择启发式算法正域约简快速约简算法粗糙近似
Keywords:
attribute reductionrough setset-valued information systemsfeature selectionheuristic algorithmpositive region reductionquick algorithm reductionrough approximations
分类号:
TP18
DOI:
10.11992/tis.201804059
摘要:
针对集值信息系统正域约简算法在大规模数据集下的运行效率问题,提出一种基于启发式的集值信息系统快速正域约简算法。通过研究属性和对象在约简过程中对算法运行效率产生的影响,在集值信息系统中引入属性无关性和属性重要度保序性的相关定义,介绍了使得算法运行效率提升的相关定理、快速算法和应用实例。通过实验对提出算法的有效性进行分析和验证。实验表明,提出算法的运行效率优于原始算法的运行效率。
Abstract:
This study aims to propose a quick positive reduction algorithm based on the heuristic method to increase the efficiency of the set-valued positive reduction algorithm under large-scale data. The definitions of attribute independence and attribute importance isotonicity are introduced in the set-valued information system by investigating the influence of an attribute and object on the efficiency of algorithm during the reduction process, and the relevant theorem, fast algorithm, and practical example for improving the efficiency of the algorithm are introduced. Finally, the experimental results show the efficiency and effectiveness of the proposed method and its better efficiency in comparison to that of the original algorithm.

参考文献/References:

[1] PAWLAK Z. Rough sets[J]. International journal of computer and information sciences, 1982, 11(5):341-356.
[2] 王国胤, 姚一豫, 于洪. 粗糙集理论与应用研究综述[J]. 计算机学报, 2009, 32(7):1229-1246 WANG Guoyin, YAO Yiyu, YU Hong. A survey on rough set theory and applications[J]. Chinese journal of computers, 2009, 32(7):1229-1246
[3] MIAO Duoqian, ZHAO Yan, YAO Yiyu, et al. Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model[J]. Information sciences, 2009, 179(24):4140-4150.
[4] LI Hua, LI Deyu, ZHAI Yanhui, et al. A novel attribute reduction approach for multi-label data based on rough set theory[J]. Information sciences, 2016, 367/368:827-847.
[5] YAO Yiyu, ZHAO Yan. Attribute reduction in decision-theoretic rough set models[J]. Information sciences, 2008, 178(17):3356-3373.
[6] JIA Xiuyi, SHANG Lin, ZHOU Bin, et al. Generalized attribute reduct in rough set theory[J]. Knowledge-based systems, 2016, 91:204-218.
[7] 张楠, 苗夺谦, 岳晓冬. 区间值信息系统的知识约简[J]. 计算机研究与发展, 2010, 47(8):1362-1371 ZHANG Nan, MIAO Duoqian, YUE Xiaodong. Approaches to knowledge reduction in interval-valued information systems[J]. Journal of computer research and development, 2010, 47(8):1362-1371
[8] HU Qinghua, ZHAO Hui, XIE Zongxia, et al. Consistency based attribute reduction[C]//Advances in Knowledge Discovery and Data Mining. Berlin, Heidelberg, Germany, 2007:96-107.
[9] GUAN Yanyong, WANG Hongkai. Set-valued information systems[J]. Information sciences, 2006, 176(17):2507-2525.
[10] QIAN Yuhua, DANG Chuanyin, LIANG Jiye, et al. Set-valued ordered information systems[J]. Information sciences, 2009, 179(16):2809-2832.
[11] 杨习贝, 张再跃, 张明. 集值信息系统中的模糊优势关系粗糙集[J]. 计算机科学, 2011, 38(2):234-237 YANG Xibei, ZHANG Zaiyue, ZHANG Ming. Fuzzy dominance-based rough set in set-valued information system[J]. Computer science, 2011, 38(2):234-237
[12] HUANG Yanyong, LI Tianrui, LUO Chuan, et al. Dynamic variable precision rough set approach for probabilistic set-valued information systems[J]. Knowledge-based systems, 2017, 122:131-147.
[13] WEI Wei, CUI Junbiao, LIANG Jiye, et al. Fuzzy rough approximations for set-valued data[J]. Information sciences, 2016, 360:181-201.
[14] ZHANG Hongying, YANG Shuyun. Feature selection and approximate reasoning of large-scale set-valued decision tables based on α-dominance-based quantitative rough sets[J]. Information sciences, 2017, 378:328-347.
[15] SKOWRON A, RAUSZER C. The discernibility matrices and functions in information systems[C]//Intelligent Decision Support Theory and Decision Library. Dordrecht, Netherlands, 1992:331-362.
[16] LUO Chuan, LI Tianrui, CHEN Hongmei, et al. Fast algorithms for computing rough approximations in set-valued decision systems while updating criteria values[J]. Information sciences, 2015, 299:221-242.
[17] ZHANG Junbo, LI Tianrui, RUAN Da, et al. Rough sets based matrix approaches with dynamic attribute variation in set-valued information systems[J]. International journal of approximate reasoning, 2012, 53(4):620-635.
[18] 刘莹莹, 吕跃进. 基于相似度的集值信息系统属性约简算法[J]. 南京大学学报(自然科学版), 2015, 51(2):384-389 LIU Yingying, LYU Yuejin. Attribute reduction in set-valued information system based on similarity[J]. Journal of Nanjing university (natural sciences), 2015, 51(2):384-389
[19] 马建敏, 张文修. 基于信息量的集值信息系统的属性约简[J]. 模糊系统与数学, 2013, 27(2):177-182 MA Jianmin, ZHANG Wenxiu. Information quantity-based attribute reduction in set-valued information systems[J]. Fuzzy systems and mathematics, 2013, 27(2):177-182
[20] 苗夺谦, 李道国. 粗糙集理论、算法与应用[M]. 北京:清华大学出版社, 2008.
[21] QIAN Yuhua, LIANG Jiye, PEDRYCZ W, et al. Positive approximation:an accelerator for attribute reduction in rough set theory[J]. Artificial intelligence, 2010, 174(9/10):597-618.
[22] 钱宇华, 梁吉业, 王锋. 面向非完备决策表的正向近似特征选择加速算法[J]. 计算机学报, 2011, 34(3):435-442 QIAN Yuhua, LIANG Jiye, WANG Feng. A positive-approximation based accelerated algorithm to feature selection from incomplete decision tables[J]. Chinese journal of computers, 2011, 34(3):435-442

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备注/Memo

备注/Memo:
收稿日期:2018-04-27。
基金项目:国家自然科学基金项目(61403329,61572418,61702439,61572419,61502410);山东省自然科学基金项目(ZR2018BA004,ZR2016FM42);烟台大学研究生科技创新基金项目(YDZD1807).
作者简介:陈曼如,女,1993年生,硕士研究生,主要研究方向为粗糙集、数据挖掘与机器学习;张楠,男,1979年生,博士研究生,主要研究方向为粗糙集、认知信息学与人工智能;童向荣,男,1975年生,教授,主要研究方向为多Agent系统、分布式人工智能与数据挖掘技术。发表学术论文50余篇,被SCI检索2篇、EI检索20余篇。
通讯作者:张楠.E-mail:zhangnan0851@163.com
更新日期/Last Update: 1900-01-01