A novel approach to fuzzy rough sets based on a fuzzy covering☆

Deng, Tingquan; Chen, Yanmei; Wei, Xu; Dai, Qionghai

doi:10.1016/j.ins.2006.11.013

Cited by 199 publications

(78 citation statements)

References 42 publications

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“…Yeung et al [190] proposed some fuzzy-rough set models by means of arbitrary fuzzy relations and investigated the connections between the existing fuzzy-rough sets. Deng et al [191] investigated fuzzy relations by involving a fuzzy covering. Li and Ma [192] proposed two pairs of fuzzy-rough approximation operators, including fuzzy and crisp covering-based fuzzyrough approximation operators.…”

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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“…In literature, different definitions of fuzzy coverings are proposed in [6,12]. However, we will use the following one, where the condition ∪C = U is maintained for infinite coverings.…”

Section: Definitionmentioning

confidence: 99%

Fuzzy Covering based Rough Sets Revisited

D'eer¹,

Cornelis²,

Sánchez³

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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In this paper we review four fuzzy extensions of the socalled tight pair of covering based rough set approximation operators. Furthermore, we propose two new extensions of the tight pair: for the first model, we apply the technique of representation by levels to define the approximation operators, while the second model is an intuitive extension of the crisp operators. For the six models, we study which theoretical properties they satisfy. Moreover, we discuss interrelationships between the models.

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“…Various extensions of rough sets, such as variable precision rough sets 54 , rough fuzzy set 3 , fuzzy rough sets 5,25,52 , etc., have been developed niques of data analysis and processing in machine learning 26 , pattern recognition 11,41 , and artificial intelligence 19 . Since redundant information usually covers a number of attributes or features in real world applications, which may confuse learning algorithms, cause distinguish slowdown in learning process and increase risk of learned classifiers to over-fit training data 50 , removing superfluous or irrelevant features is necessary in classification modeling.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection in Decision Systems Based on Conditional Knowledge Granularity

Deng¹,

Yang²,

Hu³

2011

IJCIS

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Feature selection is an important technique for dimension reduction in machine learning and pattern recognition communities. Feature evaluation functions play essential roles in constructing feature selection algorithms. This paper introduces a new notion of knowledge granularity, called conditional knowledge granularity, reflecting relationship between conditional attributes and decision attribute. An evaluation function to measure significance of conditional attributes is proposed and equivalent characterization of attribute reduction is established based on the conditional knowledge granularity. An optimal algorithm for feature selection is developed on the basis of the proposed evaluation function. Furthermore, a novel approach to performing feature selection in an inconsistent decision system is put forward through establishing a rough communication between the inconsistent decision system and a consistent decision system. Simulated experiments verifies feasibility and efficiency of the proposed technique.

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A novel approach to fuzzy rough sets based on a fuzzy covering☆

Cited by 199 publications

References 42 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

Fuzzy Covering based Rough Sets Revisited

Feature Selection in Decision Systems Based on Conditional Knowledge Granularity

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