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Soft Computing

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06.07.2017 | Foundations

Generalized rough set models determined by multiple neighborhoods generated from a similarity relation

verfasst von: Jianhua Dai, Shuaichao Gao, Guojie Zheng

Erschienen in: Soft Computing | Ausgabe 7/2018

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Abstract

Rough set theory is widely used to deal with uncertainty. Original rough set model is mainly based on equivalence relations. To extend the application scope, classical rough set model based on equivalence relations is generalized to rough set model based on similarity relations. In the present paper, we propose and investigate three new generalized rough set models by introducing new definitions of lower and upper approximations based on multiple neighborhoods generated from a similarity relation. The characteristics of the proposed approximations are investigated. Theoretically, analysis indicates the monotonicity of the corresponding uncertainty measures including accuracy, roughness and approximation accuracy. Experiments indicate that the constructed monotonic measures can be used in attribute reduction.

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Titel: Generalized rough set models determined by multiple neighborhoods generated from a similarity relation
verfasst von: Jianhua Dai
Shuaichao Gao
Guojie Zheng
Publikationsdatum: 06.07.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 7/2018
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-017-2672-x

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