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

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

2 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 of 2011 International Conference on Machine Learning and Cybernetics
Publisher	IEEE
Pages	108-112
Number of pages	5
ISBN (Electronic)	9781457703089
ISBN (Print)	9781457703058
DOIs	https://doi.org/10.1109/ICMLC.2011.6016740
Publication status	Published - Jul 2011
Externally published	Yes
Event	2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 - Guilin, Guangxi, China Duration: 10 Jul 2011 → 13 Jul 2011

Conference

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

Keywords

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

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10.1109/ICMLC.2011.6016740

Cite this

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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",

month = jul,

doi = "10.1109/ICMLC.2011.6016740",

language = "English",

isbn = "9781457703058",

pages = "108--112",

booktitle = "Proceedings of 2011 International Conference on Machine Learning and Cybernetics",

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

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YANG, Y-Y, CHEN, D-G & KWONG, S 2011, Novel algorithms of attribute reduction for variable precision rough set. in Proceedings of 2011 International Conference on Machine Learning and Cybernetics. IEEE, 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

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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.

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KW - Equivalence class pair relative to condition attributes

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KW - Variable precision rough set

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Novel algorithms of attribute reduction for variable precision rough set

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Keywords

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