[1]杨成东,邓廷权.综合属性选择和删除的属性约简方法[J].智能系统学报,2013,8(02):183-186.[doi:10.3969/j.issn.1673-4785.201209056]
 YANG Chengdong,DENG Tingquan.An approach to attribute reduction combining attribute selection and deletion[J].CAAI Transactions on Intelligent Systems,2013,8(02):183-186.[doi:10.3969/j.issn.1673-4785.201209056]
点击复制

综合属性选择和删除的属性约简方法(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第8卷
期数:
2013年02期
页码:
183-186
栏目:
出版日期:
2013-04-25

文章信息/Info

Title:
An approach to attribute reduction combining attribute selection and deletion
文章编号:
1673-4785(2013)02-0183-04
作者:
杨成东1 邓廷权2
1.临沂大学 信息学院, 山东 临沂276005;
2.哈尔滨工程大学 理学院, 黑龙江 哈尔滨150001
Author(s):
YANG Chengdong1 DENG Tingquan2
1. School of Informatics, Linyi University, Linyi 276005, China;
2. College of Science, Harbin Engineering University, Harbin 150001, China
关键词:
辨识矩阵属性约简信息冗余人工智能机器学习属性选择属性删除
Keywords:
discernibility matrix attribute reduction information redundancy artificial intelligence machine learning attribute selection attribute deletion
分类号:
TP301.6
DOI:
10.3969/j.issn.1673-4785.201209056
文献标志码:
A
摘要:
属性约简能有效地消除信息冗余,广泛应用于人工智能、机器学习.通过实例指出基于辨识矩阵的经典的属性约简方法存在不能得到约简的可能性,仍具有冗余性.因此,提出了综合属性选择和删除算法的辨识矩阵属性约简方法,并有效解决该问题.通过UCI标准数据集验证表明,新方法比经典方法进一步减少了属性的个数,凸显其实用性和有效性.
Abstract:
Attribute reduction has been defined as a method for removing information redundancy effectively, which has been widely applied to artificial intelligence, and machine learning. However, an example demonstrates classical attribute reduction approaches based on discernibility matrix may not get a reduction with redundancy. Therefore, an attribute reduction based on discernibility matrix combining attribute selection and deletion was proposed and thus, the problem was solved effectively. Moreover, UCI standard data sets provide further explanations on the feasibility, effectiveness, and as well as additional information on reducing the number of attributes without the classical approaches.

参考文献/References:

[1]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: 5-7.
[2]PAWLAK Z. Rough sets[J]. International Journal of Computer and Information Sciences, 1982, 11(5): 341-356.
[3]蒋云良, 杨章显, 刘勇. 不协调信息系统快速属性分布约简方法[J]. 自动化学报, 2012, 38(3): 382-388.  JIANG Yunliang, YANG Zhangxian, LIU Yong. Quick distribution reduction algorithm in inconsistent information system[J]. Acta Automatica Sinica, 2012, 38(3): 382-388.
[4]XU F, MIAO D, WEI L. Fuzzy-rough attribute via mutual information with an application to cancer classification[J]. Computers and Mathematics with Applications, 2009, 57(6): 1010-1017.
[5]BHATT R, GOPAL M. On fuzzy-rough sets approach to feature selection[J]. Pattern Recognition Letters, 2004, 26(7): 965-975.
[6]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 attributereduction based on neighborhood granulation and rough approximation[J]. Journal of Software, 2008, 19(3): 640-649. 
[7]TSANG E C C, CHEN D G, YEUNG D S, et al. Attribute reduction using fuzzy rough sets[J]. IEEE Transactions on Fuzzy Systems, 2008, 16(5): 1130-1141.
[8]张志飞, 苗夺谦. 基于粗糙集的文本分类特征选择算法[J]. 智能系统学报, 2009, 4(5): 453-457.  ZHANG Zhifei, MIAO Duoqian. Feature selection for text categorization based on rough set[J]. CAAI Transactions on Intelligent Systems, 2009, 4(5): 453-457.
[9]SKOWRON A, RAUSZER C. The discernibility matrices and functions in information systems[M]//Intelligent Decision Support, Handbook of Applications and Advances of the Rough Sets Theory. Dordrecht: Kluwer Academic Publishers,1992: 331-362.
[10]常犁云, 王国胤, 吴渝. 一种基于Rough Set理论的属性约简及规则提取方法[J].软件学报, 1999, 10(11): 1206-1211. 
CHANG Liyun, WANG Guoyin, WU Yu. An approach for attribute reduction and rule generation based on rough set theory[J]. Journal of Software, 1999, 10(11): 1206-1211.

相似文献/References:

[1]伞 冶,叶玉玲.粗糙集理论及其在智能系统中的应用[J].智能系统学报,2007,2(02):40.
 SAN Ye,YE Yu-ling.Rough set theory and its application in the intelligent systems[J].CAAI Transactions on Intelligent Systems,2007,2(02):40.
[2]马胜蓝,叶东毅.一种带禁忌搜索的粒子并行子群最小约简算法[J].智能系统学报,2011,6(02):132.
 MA Shenglan,YE Dongyi.A minimum reduction algorithm based on parallel particle subswarm optimization with tabu search capability[J].CAAI Transactions on Intelligent Systems,2011,6(02):132.
[3]乔丽娟,徐章艳,谢小军,等.基于知识粒度的不完备决策表的属性约简算法[J].智能系统学报,2016,11(1):129.[doi:10.11992/tis.201506029]
 QIAO Lijuan,XU Zhangyan,XIE Xiaojun,et al.Efficient attribute reduction algorithm for an incomplete decision table based on knowledge granulation[J].CAAI Transactions on Intelligent Systems,2016,11(02):129.[doi:10.11992/tis.201506029]
[4]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(02):219.[doi:10.3969/j.issn.1673-4785.201307010]
 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(02):219.[doi:10.3969/j.issn.1673-4785.201307010]
[5]韦碧鹏,吕跃进,李金海.α优势关系下粗糙集模型的属性约简[J].智能系统学报,2014,9(02):251.[doi:10.3969/j.issn.1673-4785.201307012]
 WEI Bipeng,LÜ,Yuejin,et al.Attribute reduction based on the rough set model under α dominance relation[J].CAAI Transactions on Intelligent Systems,2014,9(02):251.[doi:10.3969/j.issn.1673-4785.201307012]
[6]史德容,徐伟华.区间值模糊决策序信息系统的部分一致约简[J].智能系统学报,2016,11(4):469.[doi:10.11992/tis.201606013]
 SHI Derong,XU Weihua.Partially consistent reduction in interval-valued fuzzy ordered decision information system[J].CAAI Transactions on Intelligent Systems,2016,11(02):469.[doi:10.11992/tis.201606013]
[7]钱进,朱亚炎.面向成组对象集的增量式属性约简算法[J].智能系统学报,2016,11(4):496.[doi:10.11992/tis.201606005]
 QIAN Jin,ZHU Yayan.An incremental attribute reduction algorithm for group objects[J].CAAI Transactions on Intelligent Systems,2016,11(02):496.[doi:10.11992/tis.201606005]
[8]高学义,张楠,童向荣,等.广义分布保持属性约简研究[J].智能系统学报,2017,12(03):377.[doi:10.11992/tis.201704025]
 GAO Xueyi,ZHANG Nan,TONG Xiangrong,et al.Research on attribute reduction using generalized distribution preservation[J].CAAI Transactions on Intelligent Systems,2017,12(02):377.[doi:10.11992/tis.201704025]
[9]冯丹,黄洋,石云鹏,等.连续型数据的辨识矩阵属性约简方法[J].智能系统学报,2017,12(03):371.[doi:10.11992/tis.201704032]
 FENG Dan,HUANG Yang,SHI Yunpeng,et al.A discernibility matrix-based attribute reduction for continuous data[J].CAAI Transactions on Intelligent Systems,2017,12(02):371.[doi:10.11992/tis.201704032]

备注/Memo

备注/Memo:
收稿日期:2012-09-25.
网络出版日期:2013-04-09. 
基金项目:山东省高等学校科技计划资助项目(J12LN91); 山东省信息化与工业化融合专项课题资助项目(2012EI100). 
通信作者:杨成东.
E-mail: yangchengdong@lyu.edu.cn.
作者简介:
杨成东,男,1984年生,讲师,博士,主要研究方向为数据挖掘、粗糙集理论、智能计算.主持山东省高等学校科技计划项目等,发表学术论文十余篇.
 邓廷权,男,1965年生,教授,博士生导师,主要研究方向为模糊信息分析、数学形态学与图像分析、智能识别与计算机视觉.主持国家自然科学基金、中国博士后科学基金、黑龙江省博士后科学基金等多项科研项目.近年来,发表学术论文30余篇,其中半数被SCI、EI、ISPT等检索.
更新日期/Last Update: 2013-05-26