Communication between information systems with covering based rough sets

Wang, Changzhong; Chen, Degang; Sun, Baiqing; Hu, Qinghua

doi:10.1016/j.ins.2012.06.010

Cited by 54 publications

(45 citation statements)

References 57 publications

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“…The covering generated by a reflexive and symmetric relation is characterized. It establishes the equivalence of the unary covering and the covering with the property that the intersection of any two elements is the union of finite elements in this covering [18][19][20][21][22][23][24][25][26][27][28].…”

Section: Covering Rough Setsmentioning

confidence: 99%

“…upper approximation) as the union of covering components intersecting the set. It is multi-mapping items for inadequate information about the genuine characteristic [18][19][20][21][22][23][24][25][26][27][28]. Hybrid rough set systems have been used in different applications for feature selections [29][30][31][32][33][34], classifications [35][36][37][38][46][47][48] and image segmentation [39].…”

Section: Introductionmentioning

confidence: 99%

“…The proposed classification method at first perceives approximation spaces of the classes by setting up an arrangement of named data exploring the traits of the recognized upper and lower approximation space for every one of the classes. Right when that happens, the fitting principle is to isolate the most legitimate class executed to perceive the most appropriate class [17][18][19][20][21][22][23][24][25][26][27][28]. Covering rough set methodology is proposed for the productive classification of medical information.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED ARTICLE: Covering-based rough set classification system

Kumar

Inbarani

Azar

et al. 2016

Neural Comput & Applic

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Medical data classification is applied in intelligent medical decision support system to classify diseases into different categories. Several classification methods are commonly used in various healthcare settings. These techniques are fit for enhancing the nature of prediction, initial identification of sicknesses and disease classification. The categorization complexities in healthcare area are focused around the consequence of healthcare data investigation or depiction of medicine by the healthcare professions. This study concentrates on applying uncertainty (i.e. rough set)-based pattern classification techniques for UCI healthcare data for the diagnosis of diseases from different patients. In this study, covering-based rough set classification (i.e. proposed pattern classification approach) is applied for UCI healthcare data. Proposed CRS gives effective results than delicate pattern classifier model. The results of applying the CRS classification method to UCI healthcare data analysis are based upon a variety of disease diagnoses. The execution of the proposed covering-based rough set classification is contrasted with other approaches, such as rough set (RS)-based classification methods, Kth nearest neighbour, improved bijective soft set, support vector machine, modified soft rough set and back propagation neural network methodologies using different evaluating measures.

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Section: Covering Rough Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RETRACTED ARTICLE: Covering-based rough set classification system

Kumar

Inbarani

Azar

et al. 2016

Neural Comput & Applic

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“…Li et al [7] has earlier studied invariant characteristics of information systems under some homomorphism. Wang et al [16][17][18]20,21] have widely researched generalized information systems under homomorphisms. Zhu et al [26,27] have also done some work in this aspect.…”

Section: Introductionmentioning

confidence: 99%

Relationships between knowledge bases and related results

Liu

et al. 2015

Knowl Inf Syst

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Relationships between information systems are very important topics in the field of artificial intelligence. The concept of homomorphisms is an effective mathematical tool to study relationships between information systems. A knowledge base is a special relation information system. This paper investigates invariant characteristics of knowledge bases under the homomorphism. The fact that knowledge bases themselves are invariant and inverse invariant under the homomorphism is firstly proved. Next, some invariant characteristics and inverse invariant characteristics under the homomorphism, such as the dependency of knowledge bases, knowledge reductions, coordinate families and necessary relations, are obtained based on this fact. Finally, lattice characteristics of the dependency of knowledge bases are given. These results will be significant for establishing a framework of granular computing in knowledge bases.

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“…Yao [38] presented a framework for the formulation, interpretation, and comparison of a specific class of neighborhood systems (called 1-neighborhood systems) induced by binary relations and rough set approximations. By means of consistent functions based on neighborhoods, Wang et al [28] dealt with the reduction issues on covering decision systems. Many scholars [4,9,24,40] studied under what condition a covering of neighborhoods is a partition.…”

Section: Introductionmentioning

confidence: 99%

Applications of repeat degree to coverings of neighborhoods

Yao

Zhu

2014

Int. J. Mach. Learn. & Cyber.

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Covering of neighborhoods is an important concept in covering-based rough sets. There are many unsolved issues related to coverings of neighborhoods. The concept of repeat degree is proposed to study under what condition a covering of neighborhoods is a partition. It enables us to deal with many issues related to coverings of neighborhoods when coverings are incomplete. This paper applies repeat degree to solve some fundamental issues in coverings of neighborhoods. First, we investigate under what condition a covering of neighborhoods is equal to the reduct of the covering which induces the covering of neighborhoods. Then we study under what condition two coverings induce the same relation and the same covering of neighborhoods. Finally, we propose an approach to calculate coverings through repeat degree.

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Communication between information systems with covering based rough sets

Cited by 54 publications

References 57 publications

RETRACTED ARTICLE: Covering-based rough set classification system

RETRACTED ARTICLE: Covering-based rough set classification system

Relationships between knowledge bases and related results

Applications of repeat degree to coverings of neighborhoods

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