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

1 Citation (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
DOIs	https://doi.org/10.1109/ICMLC.2011.6016740
Publication status	Published - 2011
Externally published	Yes

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

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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.",

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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.

KW - Discernibility matrix

KW - Equivalence class pair relative to condition attributes

KW - Minimal element

KW - Variable precision rough set

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M3 - Conference paper (refereed)

SP - 108

EP - 112

BT - Proceedings - International Conference on Machine Learning and Cybernetics

ER -

Novel algorithms of attribute reduction for variable precision rough set

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Keywords

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