Multigranulation decision-theoretic rough sets

Qian, Yuhua; Hu, Zhang; Sang, Yanli; Liang, Jiye

doi:10.1016/j.ijar.2013.03.004

Cited by 330 publications

(80 citation statements)

References 46 publications

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“…Ma et al [224] suggested a decision region distribution preservation reduction in decision-theoretic rough set theory. Qian et al [225] investigated the multigranulation decision-theoretic rough set approach. Sun et al [82] examined the DTRFS application and model.…”

Section: Related Workmentioning

confidence: 99%

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

et al. 2017

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Rough set theory has been used extensively in fields of complexity, cognitive sciences, and artificial intelligence, especially in numerous fields such as expert systems, knowledge discovery, information system, inductive reasoning, intelligent systems, data mining, pattern recognition, decision-making, and machine learning. Rough sets models, which have been recently proposed, are developed applying the different fuzzy generalisations. Currently, there is not a systematic literature review and classification of these new generalisations about rough set models. Therefore, in this review study, the attempt is made to provide a comprehensive systematic review of methodologies and applications of recent generalisations discussed in the area of fuzzy-rough set theory. On this subject, the Web of Science database has been chosen to select the relevant papers. Accordingly, the systematic and metaanalysis approach, which is called "PRISMA," has been proposed and the selected articles were classified based on the author and year of publication, author nationalities, application field, type of study, study category, study contribution, and journal in which the articles have appeared. Based on the results of this review, we found that there are many challenging issues related to the different application area of fuzzy-rough set theory which can motivate future research studies.

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Section: Related Workmentioning

confidence: 99%

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

et al. 2017

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“…Yang et al studied weighted mean, optimistic and pessimistic multigranulation DTRS in incomplete information table 27 . Qian et al proposed multigranulation DTRS 25 for the fusion of different relations. However, these studies don't consider the composite information table in DTRS.…”

Section: Introductionmentioning

confidence: 99%

Dynamic composite decision-theoretic rough set under the change of attributes

Wang¹,

Yang²,

Chen³

et al. 2018

IJCIS

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In practical decision-making, we prefer to characterize the uncertain problems with the hybrid data, which consists of various types of data, e.g., categorical data, numerical dada, interval-valued data and set-valued data. The extended rough sets can deal with single type of data based on specific binary relation, including the equivalence relation, neighborhood relation, partial order relation, tolerance relation, etc. However, the fusion of these relations is a significant challenge task in such composite information table. To tackle this issue, this paper proposes the intersection and union composite relation, and further introduces a quantitative composite decision-theoretic rough set model. Subsequently, we present a novel matrix-based approach to compute the upper and lower approximations in proposed model. Moreover, we propose the incremental updating mechanisms and algorithms under the addition and deletion of attributes. Finally, experimental valuations are conducted to illustrate the efficiency of proposed method and algorithms.

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“…Nevertheless, to solve applicable problem, just only one knowledge granulation may not be fine enough. For this reason the issues of multi granulation have been absolutely discussed in many rough set studies [29][30][31][32][33][34]. From this aspect, we will propose a new dominance approach into stochastic information systems.…”

Section: Introductionmentioning

confidence: 99%

Attribute Reduction in Stochastic Information Systems Based on α-Dominance

Aydoğan

Özmen

2017

Bilişim Teknolojileri Dergisi

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Abstract-Rough set has been commonly taken part in literature to examine inadequate and incomplete information systems. The efficiency of rough set with stochastic data observed for developing convenience and scalability. In this study, we use a ranking approach for attribute reduction in stochastic information systems and generalized this via presenting a dominance relation. We obtained the rough set approach of attribute reduction in stochastic information systems by establishing the dominance degrees. Furthermore, attribute reduction methods are studied by considering discernibility matrix and this approach is applied to explanatory examples to demonstrate its validity. Also this research proposes many research fields and new application areas show a tendency to concerning rough set approach to stochastic information systems.

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Multigranulation decision-theoretic rough sets

Cited by 330 publications

References 46 publications

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

Dynamic composite decision-theoretic rough set under the change of attributes

Attribute Reduction in Stochastic Information Systems Based on α-Dominance

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