Article

Several approaches to attribute reduction in variable precision rough set model

Author:

Masahiro InuiguchiAuthors Info & Claims

MDAI'05: Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence

Pages 215 - 226

https://doi.org/10.1007/11526018_22

Published: 25 July 2005 Publication History

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Abstract

In this paper, we discuss attribute reduction in variable precision rough set model. We consider several kinds of reducts preserving some of lower approximations, upper approximations, boundary regions and the unpredictable region. We show relations among those kinds of reducts. Moreover we discuss logical function representations of the preservation of lower approximations, upper approximations, boundary regions and the unpredictable region as a basis for reduct calculation. By those discussions, the great difference between the analysis using variable precision rough sets and the classical rough set analysis is emphasized.

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Cited By

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Huang CTseng TFan YHsu C(2013)Alternative rule induction methods based on incremental object using rough set theoryApplied Soft Computing10.1016/j.asoc.2012.08.04213:1(372-389)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.1016/j.asoc.2012.08.042
Roy SGupta ASinha ARamesh RMeghanathan NWozniak M(2012)Cancer data investigation using variable precision Rough set with flexible classificationProceedings of the Second International Conference on Computational Science, Engineering and Information Technology10.1145/2393216.2393295(472-475)Online publication date: 26-Oct-2012
https://dl.acm.org/doi/10.1145/2393216.2393295
Yao YZhao YWang JHan S(2008)A model of user-oriented reduct construction for machine learningTransactions on rough sets VIII10.5555/1806237.1806252(332-351)Online publication date: 1-Jan-2008
https://dl.acm.org/doi/10.5555/1806237.1806252
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Published In

MDAI'05: Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence

July 2005

468 pages

ISBN:3540278710

Editors:
Vicenç Torra
IIIA, Artificial Intelligence Research Institute CSIC, Spanish National Research Council, Campus UAB s/n, Bellaterra, Catalonia, Spain
,
Yasuo Narukawa
IIIA, Artificial Intelligence Research Institute CSIC, Toho Gakuen, 3-1-10 Naka, Kunitachi, Tokyo, Catalonia, Japan
,
Sadaaki Miyamoto
Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, 3-1-10 Naka, Kunitachi, Ibaraki, Catalonia, Japan

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Berlin, Heidelberg

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Published: 25 July 2005

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Huang CTseng TFan YHsu C(2013)Alternative rule induction methods based on incremental object using rough set theoryApplied Soft Computing10.1016/j.asoc.2012.08.04213:1(372-389)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.1016/j.asoc.2012.08.042
Roy SGupta ASinha ARamesh RMeghanathan NWozniak M(2012)Cancer data investigation using variable precision Rough set with flexible classificationProceedings of the Second International Conference on Computational Science, Engineering and Information Technology10.1145/2393216.2393295(472-475)Online publication date: 26-Oct-2012
https://dl.acm.org/doi/10.1145/2393216.2393295
Yao YZhao YWang JHan S(2008)A model of user-oriented reduct construction for machine learningTransactions on rough sets VIII10.5555/1806237.1806252(332-351)Online publication date: 1-Jan-2008
https://dl.acm.org/doi/10.5555/1806237.1806252
Yao YZhao Y(2008)Attribute reduction in decision-theoretic rough set modelsInformation Sciences: an International Journal10.1016/j.ins.2008.05.010178:17(3356-3373)Online publication date: 1-Sep-2008
https://dl.acm.org/doi/10.1016/j.ins.2008.05.010

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Variable-precision dominance-based rough set approach and attribute reduction

The models of variable precision multigranulation rough sets

Comparative study of variable precision rough set model and graded rough set model

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