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Updating knowledge in multigranulation decision‐theoretic rough set model based on decision support degree
Author(s) -
Lin Guoping,
Liu Fengling,
Chen Shengyu,
Yu Xiaolong
Publication year - 2020
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.1192
Subject(s) - rough set , computer science , data mining , degree (music) , machine learning , artificial intelligence , dominance based rough set approach , algorithm , physics , acoustics
Based on the majority rules, a multigranulation decision‐theoretic rough set model based on the decision support degree is proposed, in which the thresholds can be computed by the decision risk minimisation based on the Bayesian decision‐theoretic. In various practical situations, information systems may alter dynamically with time. Incremental learning is an alternative manner for maintaining knowledge by utilising previous computational results under dynamic data. Therefore, the authors investigate dynamic approaches to update the knowledge in the new model when adding or deleting granular structures. Besides, the corresponding dynamic and static algorithms are designed and their time complexities are analysed. Finally, comparative experiments by using six data sets from UCI are carried out; the results illustrate that the proposed dynamic algorithm is effective and is more efficient than the static algorithm.

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