[1]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):496-502.[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(4):496-502.[doi:10.11992/tis.201606005]
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面向成组对象集的增量式属性约简算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第11卷
期数:
2016年4期
页码:
496-502
栏目:
出版日期:
2016-07-25

文章信息/Info

Title:
An incremental attribute reduction algorithm for group objects
作者:
钱进12 朱亚炎1
1. 江苏理工学院 计算机工程学院, 江苏 常州 213015;
2. 南京信息工程大学 江苏省大数据分析技术重点实验室, 江苏 南京 210044
Author(s):
QIAN Jin12 ZHU Yayan1
1. School of Computer Engineering, Jiangsu University of Technology, Changzhou 213015, China;
2. Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT, Nanjing University of Information Science & Technology, Nanjing 210044, China
关键词:
粗糙集属性约简成组对象集约简传承性增量式学习
Keywords:
rough set theoryattribute Reductiongroup objectsinheritance rate of Reductincremental learning
分类号:
TP181
DOI:
10.11992/tis.201606005
摘要:
现实世界中数据集都是动态变化的,非增量式属性约简方法从头重新计算原始数据集,而且未考虑先前约简结果中的信息,将耗费大量的时间和空间。为此,讨论了动态数据环境下约简的不变性,提出了一种面向成组对象集的增量式属性约简算法,利用先前约简中信息来快速获取强传承性的约简,从而提高增量式学习算法效率。最后,将该算法与非增量式约简方法和面向单个对象的增量式约简方法在UCI数据集和人工数据集上进行了相关比较。实验结果表明,面向成组对象的增量式属性约简算法能够快速处理动态数据,具有较好的约简传承性。
Abstract:
Real-world datasets change in size dynamically. Non-incremental attribute reduction methods usually need to re-compute source data when obtaining a new reduction without considering the information in the existing reduction, which consumes a great deal of computational time and storage space. Therefore, in this paper, some reduction invariance properties for dynamic datasets are discussed. An incremental attribute reduction algorithm for group objects using the previous reduction is proposed to quickly update a reduction with high inheritance rate and thus improve the efficiency of incremental learning. Finally, the incremental approach proposed is compared with an existing incremental attribute reduction algorithm for a single object, the non-incremental attribute reduction algorithms on the UCI, and synthetic datasets. Experimental results show that this incremental attribute reduction algorithm for group objects can deal with dynamic data rapidly, as it has better inheritance of reduction.

参考文献/References:

[1] PAWLAK Z. Rough sets[J]. International journal of computer & information sciences, 1982, 11(5):341-356.
[2] SKOWRON A, RAUSZER C. The discernibility matrices and functions in information systems[M]//SLOWINSKI R. Intelligent Decision Support, Handbook of Applications and Advances of the Rough Sets Theory. Netherlands:Springer, 1992:311-362.
[3] 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 Duoqian, HU Guirong. A heuristic algorithm for reduction of knowledge[J]. Journal of computer research and development, 1999, 36(6):681-684.
[4] 王国胤, 于洪, 杨大春. 基于条件信息熵的决策表约简[J]. 计算机学报, 2002, 25(7):759-766. WANG Guoyin, YU Hong, YANG Dachun. Decision table reduction based on conditional information entropy[J]. Chinese journal of computers, 2002, 25(7):759-766.
[5] HU Feng, WANG Guoyin, HUANG Hai, et al. Incremental attribute reduction based on elementary sets[M]//SLEZAK D, WANG Guoyin, SZCZUKA M, et al. Proceedings of the 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Berlin Heidelberg:Springer, 2005:185-193.
[6] 杨明. 一种基于改进差别矩阵的属性约简增量式更新算法[J]. 计算机学报, 2007, 30(5):815-822. YANG Ming. An incremental updating algorithm for attribute reduction based on improved discernibility matrix[J]. Chinese journal of computers, 2007, 30(5) 815-822.
[7] 冯少荣, 张东站. 一种高效的增量式属性约简算法[J]. 控制与决策, 2011, 26(4):495-500. FENG Shaorong, ZHANG Dongzhan. Effective incremental algorithm for attribute reduction[J]. Control and decision, 2011, 26(4):495-500.
[8] 尹林子, 阳春华, 王晓丽, 等. 基于标记可辨识矩阵的增量式属性约简算法[J]. 自动化学报, 2014, 40(3):397-404. YIN Linzi, YANG Chunhua, WANG Xiaoli, et al. An incremental algorithm for attribute reduction based on labeled discernibility matrix[J]. Acta automatica sinica, 2014, 40(3):397-404.
[9] SHU Wenhao, SHEN Hong. Updating attribute reduction in incomplete decision systems with the variation of attribute set[J]. International journal of approximate reasoning, 2014, 55(3):867-884.
[10] CHEN Hongmei, LI Tianrui, LUO Chuan, et al. A Decision-theoretic rough set approach for dynamic data mining[J]. IEEE transactions on fuzzy systems, 2015, 23(6):1958-1970.
[11] LIANG Jiye, WANG Feng, DANG Chuangyin, et al. A group incremental approach to feature selection applying rough set technique[J]. IEEE transactions on knowledge and data engineering, 2014, 26(2):294-308.
[12] QIAN Jin, YE Feiyue, LV Ping. An incremental attribute reduction algorithm in decision table[C]//Proceedings of the 7th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). Yantai, China:IEEE, 2010, 4:1848-1852.
[13] QIAN Jin, MIAO Duoqian, ZHANG Zehua, et al. Hybrid approaches to attribute reduction based on indiscernibility and discernibility relations[J]. International journal of approximate reasoning, 2011, 52(2):212-230.
[14] QIAN Jin, MIAO Duoqian, ZHANG Zehua, et al. Parallel attribute reduction algorithms using MapReduce[J]. Information sciences, 2014, 279:671-690.
[15] 康向平, 苗夺谦. 一种基于概念格的集值信息系统中的知识获取方法[J]. 智能系统学报, 2016, 11(3):287-293. KANG Xiangping, MIAO Duoqian. A knowledge acquisition method based on concept latticein set-valued information systems[J]. CAAI transactions on intelligent systems, 2016, 11(3):287-293.

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备注/Memo

备注/Memo:
收稿日期:2014-06-02。
基金项目:江苏省自然科学基金项目(BK20141152);教育部人文社会科学研究青年基金项目(15YJCZH129);江苏省青蓝工程项目;江苏省大数据分析技术重点实验室开放基金项目(KXK1402);江苏理工学院校级大学生创新项目(KYX15017).
作者简介:钱进,男,1975年生,副教授,博士,主要研究方向为粗糙集、粒计算、云计算、大数据等。发表学术论文40余篇,其中被SCI、EI检索20余篇;朱亚炎,男,1994年生,主要研究方向为粗糙集、云计算等。
通讯作者:钱进.E-mail:qjqjlqyf@163.com.
更新日期/Last Update: 1900-01-01