Study of S-rough set-based attribute reduction algorithms

doi:10.3778/j.issn.1002-8331.2009.34.014

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (34): 44-46.DOI: 10.3778/j.issn.1002-8331.2009.34.014

• 研究、探讨 • Previous Articles Next Articles

Study of S-rough set-based attribute reduction algorithms

TENG Bao，MA Ying－hong，GONG Yan－qing，LI Bao－yan

Department of Management and Economics，Shandong Normal University，Jinan 250014，China

Received:2008-07-08 Revised:2008-10-09 Online:2009-12-01 Published:2009-12-01
Contact: TENG Bao

基于S-粗集理论上的属性约简与算法研究

滕宝，马英红，公延庆，李宝艳

山东师范大学管理与经济学院，济南 250014

通讯作者: 滕宝

Abstract

Abstract: This PAPER puts forward the one-direction singular set，two-direction singular set；a model based on the S-rough sets information system is also given.And it discusses some features of the attribute transfer.From Skowron’s discernbility matrix point of view，discernable matrix of S-rough is proposed.Then，an attributes reduction algorithm based on the S-rough set theory for information system is rendered.Finally，the algorithm in the application of medicine is given.It has proven this algorithm validity.

Key words: singular rough set, discernbility matrix, attribute transfer, attribute reduction

摘要： 给出基于S-粗集的单向属性迁移集合，双向属性迁移集合以及单向信息系统和双向信息系统的模型；又给出属性迁移的若干性质。从Skowron提出的分辨矩阵的角度出发，给出了S-粗集中的分辨矩阵，并在此基础上提出了基于S-粗集理论上的属性约简算法。最后给出该算法在医学方面中的应用，证明了该算法的有效性。

关键词: S-粗集, 分辨矩阵, 属性迁移, 属性约简

CLC Number:

TP18

TENG Bao，MA Ying－hong，GONG Yan－qing，LI Bao－yan. Study of S-rough set-based attribute reduction algorithms[J]. Computer Engineering and Applications, 2009, 45(34): 44-46.

滕宝，马英红，公延庆，李宝艳. 基于S-粗集理论上的属性约简与算法研究[J]. 计算机工程与应用, 2009, 45(34): 44-46.

[1]	DAI Qi, LI Min, LIU Yang, LI Lihong. Fast Attribute Reduction Algorithm Based on Fuzzy Hierarchical Quotient Space [J]. Computer Engineering and Applications, 2021, 57(4): 55-60.
[2]	GAO Tianyu, WANG Qingrong, YANG Lei. Data Mining Model Based on Attribute Dependability Enhancement of Rough Set [J]. Computer Engineering and Applications, 2021, 57(3): 87-93.
[3]	ZHANG Chengling, LI Jinjin, LIN Yidong. Attribute Reduction in Formal Contexts Based on OE-Concept Lattices [J]. Computer Engineering and Applications, 2021, 57(15): 82-89.
[4]	LIU Guizhi. Incremental Attribute Reduction of Incomplete Hybrid Data Based on Dimension Change [J]. Computer Engineering and Applications, 2021, 57(12): 161-169.
[5]	MOU En, ZHANG Xianyong, YAO Yuesong, DENG Qie. Class-Specific Attribute Reduct and Its Heuristic Algorithm of Neighborhood Approximation Condition-Entropy [J]. Computer Engineering and Applications, 2020, 56(24): 175-180.
[6]	LI Xu, RONG Zijing, REN Yan. Attribute Reduction on Weighted Decision Table [J]. Computer Engineering and Applications, 2020, 56(12): 54-59.
[7]	CHEN Panpan, LIN Menglei. Attributes Reduction of Single Valued Neutrosophic Decision Information System Based on Inclusion Degree [J]. Computer Engineering and Applications, 2020, 56(12): 175-181.
[8]	WU Yangyang, TANG Jianguo. Research Progress of Attribute Reduction Based on Rough Set in Context of Big Data [J]. Computer Engineering and Applications, 2019, 55(6): 31-38.
[9]	YANG Zhen, GENG Xiuli. Research on Mining Association Rules Based on Multi-Granularity Attribute Reduction [J]. Computer Engineering and Applications, 2019, 55(6): 133-139.
[10]	LI Yun, MA Yinghong. Node Importance Rank by Attribute Reduction Set Evaluation [J]. Computer Engineering and Applications, 2019, 55(5): 149-158.
[11]	REN Xiaoxia1, XUE Fan2，3. Heuristic attribute reduction algorithm based on fuzzy neighborhood rough set [J]. Computer Engineering and Applications, 2018, 54(9): 47-53.
[12]	WANG Changbao1, YANG Xibei1，2, DOU Huili1, CHEN Xiangjian1, WANG Pingxin3. Research on local attribute reduction approach via neighborhood decision error rate [J]. Computer Engineering and Applications, 2018, 54(6): 95-99.
[13]	CHEN Jian, ZHANG Xiaohong. Approximately data detecting method based on fusion of information entropy and fuzzy integrated evaluation [J]. Computer Engineering and Applications, 2018, 54(24): 57-60.
[14]	ZHENG Na, WANG Jiayang. Evidence characteristics and attribute reduction of incomplete ordered information system [J]. Computer Engineering and Applications, 2018, 54(21): 43-47.
[15]	PENG Xiaoran1，2, LIU Zunren2, JI Jun2. Positive region computation of neighborhood rough set based on category of samples [J]. Computer Engineering and Applications, 2018, 54(20): 115-121.

Study of S-rough set-based attribute reduction algorithms

基于S-粗集理论上的属性约简与算法研究

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics