Novel algorithms of attribute reduction for variable precision rough set

Yan-Yan Yang; De-Gang Chen; Sam Kwong

doi:10.1109/ICMLC.2011.6016740

Novel algorithms of attribute reduction for variable precision rough set

Yan-Yan Yang
, De-Gang Chen
, Sam Kwong

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

3 Citations (Scopus)

Abstract

The main application of variable precision rough set is to perform attribute reduction for databases. In variable precision rough set, the approach of discernibility matrix is theoretical foundation of finding reducts. In this paper, we observe that only minimal elements in the discernibility matrix is sufficient to find reducts, and every minimal element in the discernibility matrix is determined by one equivalence class pair relative to condition attributes at least; this fact motivates our idea in this paper to search the connection between this kind of pair and the minimal element in the discernibility matrix. By the connection between them, we develop the novel algorithms of finding reducts, which improve the existing ones in terms of discernibility matrix. © 2011 IEEE.

Original language	English
Title of host publication	Proceedings - International Conference on Machine Learning and Cybernetics
Pages	108-112
Volume	1
DOIs	https://doi.org/10.1109/ICMLC.2011.6016740
Publication status	Published - 2011
Event	2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 - Guilin, Guangxi, China Duration: 10 Jul 2011 → 13 Jul 2011

Publication series

Name
Volume	1
ISSN (Print)	2160-133X
ISSN (Electronic)	2160-1348

Conference

Conference	2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
Place	China
City	Guilin, Guangxi
Period	10/07/11 → 13/07/11

Research Keywords

Discernibility matrix
Equivalence class pair relative to condition attributes
Minimal element
Variable precision rough set

Access Output

10.1109/ICMLC.2011.6016740

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@inproceedings{7e2ea064dac249889e9ca840385b1e17,

title = "Novel algorithms of attribute reduction for variable precision rough set",

abstract = "The main application of variable precision rough set is to perform attribute reduction for databases. In variable precision rough set, the approach of discernibility matrix is theoretical foundation of finding reducts. In this paper, we observe that only minimal elements in the discernibility matrix is sufficient to find reducts, and every minimal element in the discernibility matrix is determined by one equivalence class pair relative to condition attributes at least; this fact motivates our idea in this paper to search the connection between this kind of pair and the minimal element in the discernibility matrix. By the connection between them, we develop the novel algorithms of finding reducts, which improve the existing ones in terms of discernibility matrix. {\textcopyright} 2011 IEEE.",

keywords = "Discernibility matrix, Equivalence class pair relative to condition attributes, Minimal element, Variable precision rough set",

author = "Yan-Yan Yang and De-Gang Chen and Sam Kwong",

year = "2011",

doi = "10.1109/ICMLC.2011.6016740",

language = "English",

isbn = "9781457703065",

volume = "1",

pages = "108--112",

booktitle = "Proceedings - International Conference on Machine Learning and Cybernetics",

note = "2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 ; Conference date: 10-07-2011 Through 13-07-2011",

}

Yang, Y-Y, Chen, D-G & Kwong, S 2011, Novel algorithms of attribute reduction for variable precision rough set. in Proceedings - International Conference on Machine Learning and Cybernetics. vol. 1, 6016740, pp. 108-112, 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011, Guilin, Guangxi, China, 10/07/11. https://doi.org/10.1109/ICMLC.2011.6016740

Novel algorithms of attribute reduction for variable precision rough set. / Yang, Yan-Yan; Chen, De-Gang; Kwong, Sam.
Proceedings - International Conference on Machine Learning and Cybernetics. Vol. 1 2011. p. 108-112 6016740.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Novel algorithms of attribute reduction for variable precision rough set

AU - Yang, Yan-Yan

AU - Chen, De-Gang

AU - Kwong, Sam

PY - 2011

Y1 - 2011

N2 - The main application of variable precision rough set is to perform attribute reduction for databases. In variable precision rough set, the approach of discernibility matrix is theoretical foundation of finding reducts. In this paper, we observe that only minimal elements in the discernibility matrix is sufficient to find reducts, and every minimal element in the discernibility matrix is determined by one equivalence class pair relative to condition attributes at least; this fact motivates our idea in this paper to search the connection between this kind of pair and the minimal element in the discernibility matrix. By the connection between them, we develop the novel algorithms of finding reducts, which improve the existing ones in terms of discernibility matrix. © 2011 IEEE.

AB - The main application of variable precision rough set is to perform attribute reduction for databases. In variable precision rough set, the approach of discernibility matrix is theoretical foundation of finding reducts. In this paper, we observe that only minimal elements in the discernibility matrix is sufficient to find reducts, and every minimal element in the discernibility matrix is determined by one equivalence class pair relative to condition attributes at least; this fact motivates our idea in this paper to search the connection between this kind of pair and the minimal element in the discernibility matrix. By the connection between them, we develop the novel algorithms of finding reducts, which improve the existing ones in terms of discernibility matrix. © 2011 IEEE.

KW - Discernibility matrix

KW - Equivalence class pair relative to condition attributes

KW - Minimal element

KW - Variable precision rough set

UR - https://www.scopus.com/pages/publications/80155171514

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-80155171514&origin=recordpage

U2 - 10.1109/ICMLC.2011.6016740

DO - 10.1109/ICMLC.2011.6016740

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781457703065

VL - 1

SP - 108

EP - 112

BT - Proceedings - International Conference on Machine Learning and Cybernetics

T2 - 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011

Y2 - 10 July 2011 through 13 July 2011

ER -

Novel algorithms of attribute reduction for variable precision rough set

Abstract

Publication series

Conference

Research Keywords

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this