article

Parameterized rough set model using rough membership and Bayesian confirmation measures

Authors:

Salvatore Greco,

Benedetto Matarazzo,

Roman SłowińskiAuthors Info & Claims

International Journal of Approximate Reasoning, Volume 49, Issue 2

Pages 285 - 300

https://doi.org/10.1016/j.ijar.2007.05.018

Published: 01 October 2008 Publication History

Abstract

A generalization of the original definition of rough sets and variable precision rough sets is introduced. This generalization is based on the concept of absolute and relative rough membership. Similarly to variable precision rough set model, the generalization called parameterized rough set model, is aimed at modeling data relationships expressed in terms of frequency distribution rather than in terms of a full inclusion relation used in the classical definition of rough sets. However, differently from the variable precision rough set model, one or more parameters modeling the degree to which the condition attribute values confirm the decision attribute value, are considered. The properties of this extended model are investigated and compared to the classical rough set model and to the variable precision rough set model.

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Greco, S., Matarazzo, B. and Słowiński, R., Rough membership and Bayesian confirmation measures for parameterized rough sets. In: Śl'zak, D., Wang, G., Szczuka, M., Düntsch, I., Yao, Y. (Eds.), Lecture Notes in Artificial Intelligence, vol. 3641. Springer-Verlag, Berlin. pp. 314-324.

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Cited By

Chacón-Gómez FCornejo MMedina J(2023)Towards Confirmation Measures to Mixed Attribute ImplicationsGraph-Based Representation and Reasoning10.1007/978-3-031-40960-8_16(193-196)Online publication date: 11-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-40960-8_16
Majumder SKar SChakraborty M(2022)On threshold based approximations of mf-rough setsApplied Intelligence10.1007/s10489-022-03784-x53:7(7528-7545)Online publication date: 19-Jul-2022
https://dl.acm.org/doi/10.1007/s10489-022-03784-x
Ray SAgrawal SGhosh S(2021)Decision Theoretic Rough Set-Based Neighborhood for Self-Organizing MapSN Computer Science10.1007/s42979-021-00490-22:2Online publication date: 18-Feb-2021
https://dl.acm.org/doi/10.1007/s42979-021-00490-2
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Index Terms

Parameterized rough set model using rough membership and Bayesian confirmation measures
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Probabilistic reasoning
      2. Vagueness and fuzzy logic
  2. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
2. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic algorithms
    2. Probabilistic reasoning algorithms
      1. Markov-chain Monte Carlo methods
      2. Sequential Monte Carlo methods

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cover image International Journal of Approximate Reasoning

International Journal of Approximate Reasoning Volume 49, Issue 2

October, 2008

270 pages

ISSN:0888-613X

Issue’s Table of Contents

Copyright © Elsevier Inc. © 2007.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 October 2008

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Cited By

Chacón-Gómez FCornejo MMedina J(2023)Towards Confirmation Measures to Mixed Attribute ImplicationsGraph-Based Representation and Reasoning10.1007/978-3-031-40960-8_16(193-196)Online publication date: 11-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-40960-8_16
Majumder SKar SChakraborty M(2022)On threshold based approximations of mf-rough setsApplied Intelligence10.1007/s10489-022-03784-x53:7(7528-7545)Online publication date: 19-Jul-2022
https://dl.acm.org/doi/10.1007/s10489-022-03784-x
Ray SAgrawal SGhosh S(2021)Decision Theoretic Rough Set-Based Neighborhood for Self-Organizing MapSN Computer Science10.1007/s42979-021-00490-22:2Online publication date: 18-Feb-2021
https://dl.acm.org/doi/10.1007/s42979-021-00490-2
Majumder SKar SSamanta E(2020)RETRACTED ARTICLE: A fuzzy rough hybrid decision making technique for identifying the infected population of COVID-19Soft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-05451-027:5(2673-2683)Online publication date: 23-Nov-2020
https://dl.acm.org/doi/10.1007/s00500-020-05451-0
Hu M(2020)Concept Analysis Using Quantitative Structured Three-Way Rough Set ApproximationsRough Sets10.1007/978-3-030-52705-1_21(283-297)Online publication date: 29-Jun-2020
https://dl.acm.org/doi/10.1007/978-3-030-52705-1_21
Yu JLi YChen MZhang BXu W(2019)Decision-theoretic rough set in lattice-valued decision information systemJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-17211136:4(3289-3301)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.3233/JIFS-172111
Hu MDeng XYao Y(2019)An Application of Bayesian Confirmation Theory for Three-Way DecisionRough Sets10.1007/978-3-030-22815-6_1(3-15)Online publication date: 17-Jun-2019
https://dl.acm.org/doi/10.1007/978-3-030-22815-6_1
Khan MPatel V(2018)A Simple Modal Logic for Reasoning in Multigranulation Rough Set ModelACM Transactions on Computational Logic10.1145/327466419:4(1-23)Online publication date: 20-Nov-2018
https://dl.acm.org/doi/10.1145/3274664
Wang PLi Q(2017)The rough membership function based on C 10 ¯ and its applicationsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-15160832:1(279-289)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.3233/JIFS-151608
Fan BC. C. Tsang EXu WYu J(2017)Double-quantitative rough fuzzy set based decisionsInformation Sciences: an International Journal10.1016/j.ins.2016.05.035378:C(264-281)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1016/j.ins.2016.05.035
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