[1]王映龙,曾淇,钱文彬,等.变精度下不完备邻域决策系统的属性约简算法[J].智能系统学报,2017,12(03):386-391.[doi:10.11992/tis.201705027]
 WANG Yinglong,ZENG Qi,QIAN Wenbin,et al.Attribute reduction algorithm of the incomplete neighborhood decision system with variable precision[J].CAAI Transactions on Intelligent Systems,2017,12(03):386-391.[doi:10.11992/tis.201705027]
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变精度下不完备邻域决策系统的属性约简算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第12卷
期数:
2017年03期
页码:
386-391
栏目:
出版日期:
2017-06-25

文章信息/Info

Title:
Attribute reduction algorithm of the incomplete neighborhood decision system with variable precision
作者:
王映龙1 曾淇1 钱文彬2 杨珺2
1. 江西农业大学 计算机与信息工程学院, 江西 南昌 330045;
2. 江西农业大学 软件学院, 江西 南昌 330045
Author(s):
WANG Yinglong1 ZENG Qi1 QIAN Wenbin2 YANG Jun2
1. School of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 330045, China;
2. School of Software, Jiangxi Agricultural University, Nanchang 330045, China
关键词:
粗糙集理论邻域关系不完备信息系统变精度分类粗糙集粒计算多粒度约简决策粗糙集
Keywords:
rough set theoryneighborhood relationincomplete information systemvariable precision classificationgranular computingmulti-granulationreducationdecision-theoretic rough sets
分类号:
TP311
DOI:
10.11992/tis.201705027
摘要:
邻域粗糙集模型在处理完备的数值型数据中得到广泛应用,但针对不完备的数值型和符号型混合数据进行属性约简的讨论相对较少。为此,首先结合邻域粗糙集给出了可变精度模型下不完备邻域决策系统的上、下近似算子及属性约简;然后通过邻域粒化的方法构建了广义邻域下可变精度的粗糙集模型,并提出了一种属性重要度的评价方法;在此基础上,设计出了面向不完备邻域决策系统的属性约简算法,该算法可直接处理不完备的数值型和符号型混合数据;最后,通过实例分析验证了本文提出的算法能够求解出变精度下不完备邻域决策系统的属性约简结果。
Abstract:
Neighborhood rough set model has been widely used in numerical data processing complete, but the discussion of attribute reduction for numeric and symbolic mixed incomplete data is relatively small. Therefore, to resolve this problem, by combining the neighborhood rough set, first, the upper and lower approximation operators and the attribute reduction of the incomplete neighborhood decision system were analyzed based on the variable precision model. Subsequently, based on the generalized neighborhood relation, a rough set model was constructed using the neighborhood granulation method. Furthermore, a method evaluating the attribute significance degree was proposed. Based on this method, an attribute reduction algorithm for the incomplete neighborhood decision system was designed, which can deal with incomplete values directly type and symbolic mixed data. Finally, through the example analysis, the algorithm can solve the attribute reduction result of incomplete neighborhood decision system with variable precision.

参考文献/References:

[1] PAWLAK Z. Rough sets and intelligent data analysis[J]. Information sciences, 2002, 147(1): 1-12.
[2] ZHANG Junbo, WONG Jiansyuan, PAN Yi, et al, A parallel matrix-based method for computing approximations in incomplete information systems[J]. IEEE transactions on knowledge and data engineering, 2015, 27(2):326-339.
[3] WU Weizhi, QIAN Yuhua, LI Tongjun, et al. On rule acquisition in incomplete multi-scale decision tables[J]. Information sciences, 2017, 378: 282-302.
[4] 张文修, 吴伟志, 梁吉业, 等. 粗糙集理论与方法[M]. 北京:科学出版社, 2001: 123-131.
[5] 刘芳,李天瑞. 基于边界域的不完备信息系统属性约简方法[J]. 计算机科学, 2016, 43(3): 242-245.LIU Fang, LI Tianrui. Method for attribute reduction based on rough sets boundary regions[J]. Computer science, 2016, 43(3): 242-245.
[6] WU Jianrong, KAI Xuewen, LI Jiaojiao. Atoms of monotone set-valued measures and integrals[J]. Fuzzy sets and systems, 2015, 183: 972-979.
[7] 王国胤, 张清华. 不同知识粒度下粗糙集的不确定性研究[J]. 计算机学报,2008, 31(9):1588-1598.WANG Guoyin, ZHANG Qinghua. Uncertainty of rough set in different knowledge granularities[J]. Chinese journal of computers, 2008, 31(9): 1588-1598.
[8] 钱文彬,杨炳儒,谢永红,等. 一种基于属性度量的快速属性约简算法[J]. 小型微型计算机系统, 2014, 35(6): 1407-1411.QIAN Wenbin, YANG Bingru, XIE Yonghong, et al. A quick algorithm for attribute reduction based on attribute measure[J]. Journal of chinese computer systems, 2014, 35(6): 1407-1411.
[9] 鞠恒荣, 马兴斌, 杨习贝, 等. 不完备信息系统中测试代价敏感的可变精度分类粗糙集[J]. 智能系统学报, 2014, 9(2):219-223.JU Hengrong, MA Xingbin, YANG Xibei, et al. Test-cost-sensitive based variable precision classification rough set in incomplete information system[J]. CAAI transactions on intelligent systems, 2014, 9(2): 219-223.
[10] 陈昊, 杨俊安, 庄镇泉. 变精度粗糙集的属性核和最小属性约简算法[J]. 计算机学报, 2012, 35(5): 1011-1017.CHEN Hao, YANG Junan, ZHUANG Zhenquan. The core of attributes and minimal attributes reduction in variable precision rough set[J]. Chinese journal of computers, 2012, 35(5):1011-1017.
[11] 张清华,薛玉斌,王国胤. 粗糙集的最优近似集[J]. 软件学报, 2016, 27(2):295-308.ZHANG Qinghua, XUE Yubin, WANG Guoyin. Optimal approximate sets of rough sets[J]. Journal of software, 2016, 27(2): 295-308.
[12] 孟慧丽,马媛媛,徐久成. 基于下近似分布粒度熵的变精度悲观多粒度粗糙集粒度约简[J]. 计算机科学, 2016, 43(2): 83-85,104.MENG Huili, MA Yuanyuan, XU Jiucheng. Granularity reduct of variable precision pessimistic multi-granulation rough set based on granularity entropy of lower approximate distribution[J]. Computer science, 2016, 43(2): 83-85,104.
[13] 续欣莹, 刘海涛, 谢珺, 等. 信息观下基于不一致邻域矩阵的属性约简[J]. 控制与决策, 2016, 31(1):130-136.XU Xinying, LIU Haitao, XIE Jun, et al. Attribute reduction based on inconsistent neighborhood matrix under information view[J]. Control and decision, 2016, 31(1): 130-136.
[14] 胡清华,于达仁, 谢宗霞. 基于邻域粒化和粗糙逼近的数值属性约简[J]. 软件学报, 2008, 19(3): 640-649.HU Qinghua, YU Daren, XIE Zongxia. Numerical attribute reduction based on neighborhood granulation and rough approximation[J]. Journal of software, 2008, 19(3): 640-649.
[15] QIAN Yuhua, LI Shunyong, LIANG Jiye. Pessimistic rough set based decisions: a multigranulation fusion strategy[J]. Information sciences, 2014, 264: 196-210.
[16] LIN Guoping, QIAN Yuhua, LI Jinjin. Neighborhood based multigranulation rough sets[J]. International journal of approximate reasoning, 2012, 7(53): 1080-1093.
[17] 沈家兰, 汪小燕, 申元霞. 可变程度多粒度粗糙集[J]. 小型微型计算机系统, 2016, 37(05): 1012-1016. SHEN Jialan, WANG Xiaoyan, SHEN Yuanxia. Variable Grade multi-granulation rough set [J]. Journal of Chinese computer systems, 2016, 37(5): 1012-1016.
[18] 许韦,吴陈,杨习贝. 基于容差关系的不完备可变精度多粒度粗糙集[J]. 计算机应用研究, 2013, 30(6):1712-1715.XU Wei, WU Chen, YANG Xibei. Incomplete variable precision multigranularity rough set based on tolerance relation[J]. Application research of computers, 2013, 30(6):1712-1715.
[19] 徐久成, 张灵均, 孙林, 等. 广义邻域关系下不完备混合决策系统的约简[J]. 计算机科学, 2013, 40(4): 244-248.XU Jiucheng, ZHANG Lingjun, SUN Lin, et al. Reduction in incomplete hybrid decision systems based on generalized neighborhood relationship[J]. Computer science, 2013, 40(4): 244-248.

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备注/Memo

备注/Memo:
收稿日期:2017-05-19。
基金项目:国家自然科学基金项目(61502213,71461013,61462038);江西省自然科学基金项目(20151BAB217009,20132BAB201045);江西省教育厅科学技术项目(GJJ150399,GJJ150505).
作者简介:王映龙,男,1970年生,教授,博士,主要研究方向为知识发现、数据挖掘和计算智能;曾淇,女,1991年生,硕士研究生,主要研究方向为粗糙集理论与知识发现;钱文彬,男,1984年生,讲师,博士,主要研究方向为粗糙集、粒计算与知识发现。
通讯作者:钱文彬.E-mail:qianwenbin1027@126.com.
更新日期/Last Update: 2017-06-25