属性约简簇的优化选择

doi:10.3969/j.issn.0372-2112.2019.05.019

电子学报 ›› 2019, Vol. 47 ›› Issue (5): 1111-1120.DOI: 10.3969/j.issn.0372-2112.2019.05.019

属性约简簇的优化选择

邓大勇^1,2, 葛雅雯², 黄厚宽³

1. 浙江师范大学行知学院, 浙江金华 321004;
2. 浙江师范大学数理与信息工程学院, 浙江金华 321004;
3. 北京交通大学计算机与信息技术学院, 北京 100044

收稿日期:2018-06-10 修回日期:2018-09-06 出版日期:2019-05-25 发布日期:2019-05-25
作者简介:邓大勇 男,1968年出生,副教授,博士,现为浙江师范大学行知学院教师,主要研究方向为粗糙集、粒计算、数据挖掘等.E-mail:dayongd@163.com;葛雅雯 女,1992年生,硕士研究生.主要研究方向为粗糙集、数据挖掘.E-mial:541865527@qq.com;黄厚宽 男,1940年生,教授,博士生导师.主要方向为计算智能、数据挖掘和粗糙集等.E-mial:hkhuang@bjtu.edu.cn
基金资助:
国家自然科学基金（No.61473030）；浙江省自然科学基金（No.LY15F020012）；浙江师范大学网络空间安全浙江省一流学科

An Optimizing Selection in a Family of Attribute Reducts

DENG Da-yong^1,2, GE Ya-wen², HUANG Hou-kuan³

1. Xingzhi College, Zhejiang Normal University, Jinhua, Zhejiang 321004, China;
2. College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua, Zhejiang 321004, China;
3. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China

Received:2018-06-10 Revised:2018-09-06 Online:2019-05-25 Published:2019-05-25

摘要/Abstract

摘要： 属性约简是粗糙集的一个重要应用.一个数据集往往含有多个属性约简，人们一般用启发式算法找到其中的一个，再通过实验的方法验证其有效性.面对多个属性约简，人们往往难以区别，缺乏有效的手段选取最优或较优的属性约简.使用多种概念漂移的度量指标和信息损失的度量方法比较了同一个知识系统中不同Pawlak约简之间的区别与联系.提出了属性约简重心的概念，并研究其性质.实验结果显示，在众多的属性约简中，离重心最近的属性约简在分类准确率方面具有较大的优势.概念漂移的度量指标和信息损失的度量方法有助于区分不同的属性约简，属性约简的重心有助于在众多的属性约简中选择最优或较优的一个.

关键词: 粗糙集, 属性约简, 概念漂移, 属性约简重心

Abstract: Attribute reduction is one of important applications in rough set theory.There are more than one attribute reduct in a data set,and heuristic algorithms are always used to find one of them,which is verified with experiments.For many attribute reducts,it is hard for people to distinguish them,and lacks of valid methods of selecting the best one or a better one.Indexes of concept drift and information loss are employed to compare the same type of Pawlak attribute reducts in a knowledge system.The focus of attribute reducts is presented,and its properties are investigated in this paper.Experimental results show that the closest attribute reduct to the focus of attribute reducts is better than other attribute reducts in classification accuracy.Indexes of concept drift detection and information loss can distinguish different attribute reducts,and the focus of attribute reducts can be employed to select the best attribute reduct or a better one.

Key words: rough sets, attribute reduction, concept drift, focus of attribute reducts

中图分类号:

TP18

邓大勇, 葛雅雯, 黄厚宽. 属性约简簇的优化选择[J]. 电子学报, 2019, 47(5): 1111-1120.

DENG Da-yong, GE Ya-wen, HUANG Hou-kuan. An Optimizing Selection in a Family of Attribute Reducts[J]. Acta Electronica Sinica, 2019, 47(5): 1111-1120.

参考文献

[1] Pawlak Z.Rough Sets-Theoretical Aspect of Reasoning About Data[M].Dordrecht:Kluwer Academic Publishers,1991.
[2] Ziarko W,Variable precision rough set model[J].Journal of Computer and System Sciences,1993,46(1):39-59.
[3] Zhu W.Wang F.Reduction and axiomization of covering generalized rough sets[J].Information Sciences,2003,152(1):217-230.
[4] Lin T Y.Neighborhood Systems:A Qualitative Theory for Fuzzy and Rough Sets[M].Duke University,Durham:Advances in Machine Intelligence and Soft Computing,Department of Electrical Engineering,1997.132-155.
[5] Zhu P,Hu Q,Zuo W,et al.Multi-granularity distance metric learning via neighborhood granule margin maximization[J].Information Sciences,2014,282(282):321-331.
[6] Qian Y H,Liang J Y,Yao Y Y,et al.MGRS:A multi-granulation rough set[J].Information Sciences,2010,180(6):949-970.
[7] 史开泉,崔玉泉.S-粗集和它的一般结构[J].山东大学学报(理学版),2002,37(6):471-474. Shi K Q,Cui Y Q.S-rough set and its general structures[J].Journal of Shandong University(Nat Sci),2002,37(6):471-474.(in Chinese)
[8] 邓大勇,陈林.并行约简与F-粗糙集.云模型与粒计算[M].北京:科学出版社,2012.210-228. Deng D Y,Chen L.Parallel Reducts and F-rough Sets.Cloud Model and Granular Computing[M].Beijing:Science Press,2012.210-228.(in Chinese)
[9] 邓大勇,卢克文,苗夺谦,黄厚宽.知识系统中全粒度粗糙集及概念漂移的研究[J].计算机学报,2016.39:在线出版号No.177. Deng Dayong,Lu kewen,Miao Duoqian,Huang Houkuan.Study on entire-granulation rough sets and concept drifting in a knowledge system[J].Chinese Journal of Computers,2016,39:Online Publishing No.177.(in Chinese)
[10] 苗夺谦,胡桂荣.知识约简的一种启发式算法[J].计算机研究与发展,1999,36(6):681-684. Miao D Q,Hu G R.A heuristic algorithm for reduction of knowledge[J].Journal of Computer Research & Development,1999,36(6):681-684.(in Chinese)
[11] Skowron A,Rauszer C.The Discernibility Matrices and Functions in Information Systems[M].Netherlands Springer,1992.
[12] Bazan G J.A comparison of dynamic non-dynamic rough set methods for extracting laws from decision tables[A].Rough Sets in Knowledge Discovery 1:Methodology and Applications,Physica-Verlag[C].Heidelberg,1998.321-365.
[13] Kryszkiewicz M.Comparative study of alternative types of knowledge reduction in inconsistent systems[J].International Journal of Intelligent Systems,2001,16(1):105-120.
[14] 邓大勇,黄厚宽,李向军.不一致决策系统中约简之间的比较[J].电子学报,2007,35(2):252-255. DENG D Y,HUANG H K,LI X J.Comparison of various types of reductions in inconsistent systems[J].Acta Electronica Sinica,2007,35(2):252-255.(in Chinese)
[15] 徐章艳,杨炳儒,宋威,等.几种不同属性约简的比较研究[J].小型微型计算机系统,2008,29(5):848-853. Xu Z Y,Yang B R,Song W,et al.Comparative research of different attribute reduction definitions[J].Journal of Chinese Computer Systems,2008,29(5):848-853.(in Chinese)
[16] 李磊军,米据生.信息系统属性约简的比较研究[J].计算机科学与探索,2009,3(6):577-584. Li L J,Mi J S.A comparative study of attribute reduction in information systems[J].Journal of Frontiers of Computer Science and Technology,2009,3(6):577-584.(in Chinese)
[17] 张东娜,刘博.三种属性约简方法的比较研究[J].电脑知识与技术:学术交流,2008,1(2):613-614. Zhang D N,Liu B.The comparison research of three reduction methods[J].Computer Knowledge and Technology,2008,1(2):613-614.(in Chinese)
[18] Wong S K M,Ziarko W.On optimal decision rules in decision tables[J].Bulletin of the Polish Academy of Sciences Mathematics,1985,33(11-12):693-696.
[19] 邓大勇,裴明华,黄厚宽.F-粗糙集方法对概念漂移的度量[J].浙江师范大学学报(自然科学版),2013,36(3):303-308. Deng D Y,Pei M H,Huang H K.The F-rough sets approaches to the measures of concept drift[J].Journal of Zhejiang Normal University(Nat Sci),2013,36(3):303-308. (in Chinese)
[20] 邓大勇,薛欢欢,苗夺谦,等.属性约简准则与约简信息损失的研究[J].电子学报,2017,45(2):401-407. Deng D Y,Xue H H,Miao D Q,et al.Study on criteria of attribute reduction and information loss of attribute reduction[J].Acta Electronica Sinica,2017,45(2):401-407.(in Chinese)
[21] 邓大勇,卢克文,黄厚宽,等.概念的属性约简及异构数据概念漂移探测[J].电子学报,2018,46(5):1234-1239. Deng D Y,Lu K W,Huang H K,et al.Attribute reduction for concepts and concept drifting detection in heterogeneous data[J].Acta Electronica Sinica,2018,46(5):1234-1239.(in Chinese)

属性约简簇的优化选择

An Optimizing Selection in a Family of Attribute Reducts

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