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article

Attribute reduction in decision-theoretic rough set models

Authors:

Yan ZhaoAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 178, Issue 17

Pages 3356 - 3373

https://doi.org/10.1016/j.ins.2008.05.010

Published: 01 September 2008 Publication History

Abstract

Rough set theory can be applied to rule induction. There are two different types of classification rules, positive and boundary rules, leading to different decisions and consequences. They can be distinguished not only from the syntax measures such as confidence, coverage and generality, but also the semantic measures such as decision-monotocity, cost and risk. The classification rules can be evaluated locally for each individual rule, or globally for a set of rules. Both the two types of classification rules can be generated from, and interpreted by, a decision-theoretic model, which is a probabilistic extension of the Pawlak rough set model. As an important concept of rough set theory, an attribute reduct is a subset of attributes that are jointly sufficient and individually necessary for preserving a particular property of the given information table. This paper addresses attribute reduction in decision-theoretic rough set models regarding different classification properties, such as: decision-monotocity, confidence, coverage, generality and cost. It is important to note that many of these properties can be truthfully reflected by a single measure @c in the Pawlak rough set model. On the other hand, they need to be considered separately in probabilistic models. A straightforward extension of the @c measure is unable to evaluate these properties. This study provides a new insight into the problem of attribute reduction.

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Cited By

Wang CWang CQian YLeng Q(2024)Feature Selection Based on Weighted Fuzzy Rough SetsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.338757132:7(4027-4037)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2024.3387571
Hou JYao Y(2024)Three-Way Cost-Performance Approximate Attribute ReductionRough Sets10.1007/978-3-031-65668-2_4(47-58)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1007/978-3-031-65668-2_4
Shi CHu MLi QYao Y(2024)Approximate Criterion Reduction in Multi-criteria Trilevel Ranking AnalysisRough Sets10.1007/978-3-031-65668-2_12(165-180)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1007/978-3-031-65668-2_12
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Index Terms

Attribute reduction in decision-theoretic rough set models
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Logical and relational learning
        Inductive logic learning
2. Theory of computation
  1. Logic
    1. Type theory

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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 178, Issue 17

September, 2008

173 pages

ISSN:0020-0255

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Copyright © © 2008.

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Elsevier Science Inc.

United States

Publication History

Published: 01 September 2008

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Cited By

Wang CWang CQian YLeng Q(2024)Feature Selection Based on Weighted Fuzzy Rough SetsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.338757132:7(4027-4037)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2024.3387571
Hou JYao Y(2024)Three-Way Cost-Performance Approximate Attribute ReductionRough Sets10.1007/978-3-031-65668-2_4(47-58)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1007/978-3-031-65668-2_4
Shi CHu MLi QYao Y(2024)Approximate Criterion Reduction in Multi-criteria Trilevel Ranking AnalysisRough Sets10.1007/978-3-031-65668-2_12(165-180)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1007/978-3-031-65668-2_12
Liang BLu Z(2023)Attribute reduction algorithm based on combined distance in clusteringJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22266645:1(1481-1496)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/JIFS-222666
Gong ZZhang J(2023)Granular reduction in formal fuzzy contexts: graph representation, graph approach and its algorithmArtificial Intelligence Review10.1007/s10462-023-10523-256:12(15101-15127)Online publication date: 15-Jun-2023
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Li XTang JHu BLi Y(2022)Indiscernibility and Discernibility Relations Attribute Reduction with Variable PrecisionScientific Programming10.1155/2022/50779682022Online publication date: 1-Jan-2022
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Zhang XWang JZhan JDai J(2022)Fuzzy Measures and Choquet Integrals Based on Fuzzy Covering Rough SetsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2021.308191630:7(2360-2374)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1109/TFUZZ.2021.3081916
Sowkuntla PPrasad P(2022)MapReduce based parallel fuzzy-rough attribute reduction using discernibility matrixApplied Intelligence10.1007/s10489-021-02253-152:1(154-173)Online publication date: 1-Jan-2022
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Mandal PSamanta SPal MRanadive A(2022)Three-way decision model under a large-scale group decision-making environment with detecting and managing non-cooperative behaviors in consensus reaching processArtificial Intelligence Review10.1007/s10462-021-10133-w55:7(5517-5542)Online publication date: 1-Oct-2022
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Wang FWei WLiang J(2022)A group incremental approach for feature selection on hybrid dataSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-06838-x26:8(3663-3677)Online publication date: 1-Apr-2022
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