Valued Dominance-Based Rough Set Approach to Incomplete Information System

Yang, Xibei; Dou, Huili

doi:10.1007/978-3-642-22619-9_5

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…The rough sets with fuzzy preferences were also proposed by the same authors [20]. The following articles include further information on current developments in fuzzy rough sets: [21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework of Pythagorean Fuzzy Dominance-Based Rough Sets and Analysis of Knowledge Reductions

Haq,

Shaheen,

Toor

et al. 2023

IEEE Access

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The dominance-based rough set approach is crucial to the advancement of rough set theory. It gives a more thorough and adaptable framework for knowledge acquisition, information analysis, and DM. It is a means of expressing discrepancies resulting from the examination of the domains with specified preference rankings of the characteristics. This work seeks to extend the Rough set approach utilizing dominance relationships to a Pythagorean fuzzy setting. The lower and upper approximations of Pythagorean fuzzy dominance-based rough set are determined by using the constructive technique. Next, we examine the basic characteristics for the rough estimations relying on the Pythagorean fuzzy dominance. By combining Approximate Distribution Reductions with a Pythagorean fuzzy dominance-based rough set, reductions are prescribed in four distinct manners. Additionally, the discernibility matrices and theorems connected to these reductions are produced. Such findings are all Pythagorean fuzzy generalizations or extensions of the conventional rough set method relying on dominance. Finally, the conceptual ideas are supported with a numerical example.

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Section: Introductionmentioning

confidence: 99%

A Novel Framework of Pythagorean Fuzzy Dominance-Based Rough Sets and Analysis of Knowledge Reductions

Haq,

Shaheen,

Toor

et al. 2023

IEEE Access

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“…It is too strict to be practical because maximum and minimum attribute values are hard to known. Yang et al introduced a valued dominance relation considering the probability that an object is dominating another one by using the information given unknown values 28 . In order to avoid semantic contradiction on the ordered relation, Gou et al proposed confidential dominance relation to extend DRSA to deal with missing data in Incomplete Ordered Decision System (IODS) 29 .…”

Section: Introductionmentioning

confidence: 99%

Incremental approximation computation in incomplete ordered decision systems

Gou¹,

Wang²

2017

IJCIS

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Approximation computation is a critical step in rough sets theory used in knowledge discovery and other related tasks. In practical applications, an information system often evolves over time by the variation of attributes or objects. Effectively computing approximations is vital in data mining. Dominance-based rough set approach can handle information with preference-ordered attribute domain, but it is not able to handle the situation of data missing. Confidential Dominance-based Rough Set Approach (CDRSA) is introduced to process Incomplete Ordered Decision System (IODS). This paper focuses on incremental updating approximations under dynamic environment in IODS. With the CDRSA, the principles of incremental updating approximations are discussed while the variation of attribute sets or the union of subsets of objects and the corresponding incremental algorithms are developed. Comparative experiments on data sets of UCI and results show that the proposed incremental approaches can improve the performance of updating approximations effectively by a significant shortening of the computational time.

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“…Ouyang et al [43] presented a fuzzy rough model, which is based on the fuzzy tolerance relation. More details about recent advancements of fuzzy rough set can be found in the literatures [9], [10], [16], [40], [41], [57], [62], [69], [70], [72].…”

Section: Introductionmentioning

confidence: 99%

Intuitionistic Fuzzy Dominance–based Rough Set Approach: Model and Attribute Reductions

Zhang¹,

Yang²

2012

JSW

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The dominance-based rough set approach plays an important role in the development of the rough set theory. It can be used to express the inconsistencies coming from consideration of the preference-ordered domains of the attributes. The purpose of this paper is to further generalize the dominance-based rough set model to fuzzy environment. The constructive approach is used to define the intuitionistic fuzzy dominance-based lower and upper approximations respectively. Basic properties of the intuitionistic fuzzy dominance-based rough approximations are then examined. By introducing the concept of approximate distribution reducts into intuitionistic fuzzy dominancebased rough approximations, four different forms of reducts are defined. The judgment theorems and discernibility matrixes associated with these reducts are also obtained. Such results are all intuitionistic fuzzy generalizations of the classical dominance-based rough set approach. Some numerical examples are employed to substantiate the conceptual arguments.

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Valued Dominance-Based Rough Set Approach to Incomplete Information System

Cited by 4 publications

References 23 publications

A Novel Framework of Pythagorean Fuzzy Dominance-Based Rough Sets and Analysis of Knowledge Reductions

A Novel Framework of Pythagorean Fuzzy Dominance-Based Rough Sets and Analysis of Knowledge Reductions

Incremental approximation computation in incomplete ordered decision systems

Intuitionistic Fuzzy Dominance–based Rough Set Approach: Model and Attribute Reductions

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