article

Extended rough set-based attribute reduction in inconsistent incomplete decision systems

Authors:

Zhongzhi ShiAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 204

Pages 44 - 69

https://doi.org/10.1016/j.ins.2012.04.004

Published: 01 October 2012 Publication History

Abstract

A systematic study of attribute reduction in inconsistent incomplete decision systems (IIDSs) has not yet been performed, and no complete methodology of attribute reduction has been developed for IIDSs to date. In an IIDS, there are various ways to handle missing values. In this paper, a missing attribute value may be replaced with any known value of a corresponding attribute (such a missing attribute value is called a ''do not care'' condition). In this way, this paper establishes reduction concepts specifically for IIDSs, mainly by extending related reduction concepts from other types of decision systems into IIDSs, and then derives their relationships and properties. With these derived properties, the extended reducts are divided into two distinct types: heritable reducts and nonheritable reducts, and algorithms for computing them are presented. Using the relationships derived here, the eight types of extended reducts established for IIDSs can be converted to five equivalent types. Then five discernibility function-based approaches are proposed, each for a particular kind of reduct. Each approach can find all reducts of its associated type. The theoretical analysis of the proposed approaches is described in detail. Finally, numerical experiments have shown that the proposed approaches are effective and suitable for handling both numerical and categorical attributes, but that they have different application conditions. The proposed approaches can provide a solution to the reduction problem for IIDSs.

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Cited By

Chen YChi F(2021)Summarization of information systems based on rough set theoryJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-20116040:1(1001-1015)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/JIFS-201160
Thuy NWongthanavasu S(2020)An efficient stripped cover-based accelerator for reduction of attributes in incomplete decision tablesExpert Systems with Applications: An International Journal10.1016/j.eswa.2019.113076143:COnline publication date: 1-Apr-2020
https://dl.acm.org/doi/10.1016/j.eswa.2019.113076
Xu YHu S(2019)Extended rough set model based on modified data-driven valued tolerance relationJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-1865836:2(1615-1625)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.3233/JIFS-18658
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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 204, Issue

October, 2012

92 pages

ISSN:0020-0255

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Copyright © Elsevier Inc. © 2012.

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Elsevier Science Inc.

United States

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Published: 01 October 2012

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Cited By

Chen YChi F(2021)Summarization of information systems based on rough set theoryJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-20116040:1(1001-1015)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/JIFS-201160
Thuy NWongthanavasu S(2020)An efficient stripped cover-based accelerator for reduction of attributes in incomplete decision tablesExpert Systems with Applications: An International Journal10.1016/j.eswa.2019.113076143:COnline publication date: 1-Apr-2020
https://dl.acm.org/doi/10.1016/j.eswa.2019.113076
Xu YHu S(2019)Extended rough set model based on modified data-driven valued tolerance relationJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-1865836:2(1615-1625)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.3233/JIFS-18658
Ge HLi LXu YYang C(2017)Quick general reduction algorithms for inconsistent decision tablesInternational Journal of Approximate Reasoning10.1016/j.ijar.2016.11.01682:C(56-80)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.ijar.2016.11.016
Meng ZGan QShi Z(2016)On efficient methods of computing attribute-value blocks in incomplete decision systemsKnowledge-Based Systems10.1016/j.knosys.2016.09.025113:C(171-185)Online publication date: 1-Dec-2016
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Meng ZShi Z(2016)On quick attribute reduction in decision-theoretic rough set modelsInformation Sciences: an International Journal10.1016/j.ins.2015.09.057330:C(226-244)Online publication date: 10-Feb-2016
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Teng SLu MYang AZhang JNian YHe M(2016)Efficient attribute reduction from the viewpoint of discernibilityInformation Sciences: an International Journal10.1016/j.ins.2015.07.052326:C(297-314)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.ins.2015.07.052
Lobato FSales CAraujo ITadaiesky VDias LRamos LSantana A(2015)Multi-objective genetic algorithm for missing data imputationPattern Recognition Letters10.1016/j.patrec.2015.08.02368:P1(126-131)Online publication date: 15-Dec-2015
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Chen JLin YLin GLi JMa Z(2015)The relationship between attribute reducts in rough sets and minimal vertex covers of graphsInformation Sciences: an International Journal10.1016/j.ins.2015.07.008325:C(87-97)Online publication date: 20-Dec-2015
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