Information structures in an incomplete information system: A granular computing viewpoint

Yu, Guangji

doi:10.2991/ijcis.11.1.89

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…Equally, information structures in an IS are also granular structures in the meaning of GrC. Yu [36] proposed information structures in an IIS. Zhang et al [45] investigated information structures and uncertainty measures in a fully fuzzy IS.…”

Section: Research Backgroundmentioning

confidence: 99%

Measures of uncertainty for a four-hybrid information system and their applications

2022

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A four-hybrid information system (4HIS) is an information system (IS) where the dataset of object descriptions consists of categorical, boolean, real-valued and missing data or attributes. This paper studies measures of uncertainty for a 4HIS and its application in attribute reduction. The distance function for each type of attribute in a 4HIS is first provided. Then, this distance is used to produce the tolerance relation induced by a given subsystem in a 4HIS. Next, information structure of this subsystem is proposed in terms of a set vector and dependence between information structures is introduced. Moreover, granulation and entropy measures in a 4HIS are investigated on the basis of information structures. In order to verify the feasibility of the proposed measures, effectiveness analysis is performed from a statistical perspective. Finally, an application of the proposed measures for attribute reduction in a 4HIS is given.

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Section: Research Backgroundmentioning

confidence: 99%

Measures of uncertainty for a four-hybrid information system and their applications

2022

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“…However, discretizing the continuous values exists some uncertainty and may lose some essential information. To solve this problem, many rough set models have been proposed, such as fuzzy rough sets [3][4][5][6], covering rough sets [7][8][9], semimonolayer cover rough set [10], neighborhood rough sets [11][12][13][14], granule-based rough sets [15][16][17]. Neighborhood rough set is a feasible model to handle continuous values without discretization.…”

Section: Introductionmentioning

confidence: 99%

Attribute Reduction of Boolean Matrix in Neighborhood Rough Set Model

Gao¹,

Lv²,

Wu³

2020

IJCIS

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Neighborhood rough set is a powerful tool to deal with continuous value information systems. Graphics processing unit (GPU) computing can efficiently accelerate the calculation of the attribute reduction and approximation sets based on matrix. In this paper, we rewrite neighborhood approximation sets in the matrix-based form. Based on the matrix-based neighborhood approximation sets, we propose the relative dependency degree of attributes and the corresponding algorithm (DBM). Furthermore, we design the reduction algorithm (ARNI) for continuous value information systems. Compared with other algorithms, ARNI can effectively remove redundant attributes, and less affect the classification accuracy. On the other hand, the experiment shows ARNI based on the matrixing rough set model can significantly speed up by GPU. The speedup is many times over the central processing unit implementation.

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“…Yu. [25] studied information structures in an incomplete IS. Their findings have been proven to be useful for knowledge discovery in ISs or knowledge bases [26].…”

Section: Introductionmentioning

confidence: 99%

Information Structures in an Incomplete Interval-Valued Information System

Zeng¹,

Li²,

Li³

et al. 2019

IJCIS

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An incomplete interval-valued information system (IIVIS) is an information system (IS) in which the information values are interval numbers with missing values. This article researches information structures in an IIVIS. First, information structures in an IIVIS are obtained. In addition, the dependence and information distance are presented. The properties of information structures are investigated. Furthermore, group and lattice characterizations of information structures in an IIVIS are studied. Lastly, the-rough entropy is explored as an application of the proposed information structures.

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Information structures in an incomplete information system: A granular computing viewpoint

Cited by 6 publications

References 17 publications

Measures of uncertainty for a four-hybrid information system and their applications

Measures of uncertainty for a four-hybrid information system and their applications

Attribute Reduction of Boolean Matrix in Neighborhood Rough Set Model

Information Structures in an Incomplete Interval-Valued Information System

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