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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 1/2017

26.11.2014 | Original Article

Incremental approaches to knowledge reduction based on characteristic matrices

verfasst von: Guangming Lang, Qingguo Li, Mingjie Cai, Tian Yang, Qimei Xiao

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2017

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Abstract

Knowledge reduction is complicated with the dynamic change of the object set in applications. In this paper, we propose incremental approaches to computing the type-1 and type-2 characteristic matrices of coverings with respect to variation of objects. Also we present two incremental algorithms of calculating the second and sixth lower and upper approximations of sets when adding and deleting more objects in dynamic covering approximation spaces. Subsequently, we employ experiments to validate that the incremental approaches are more effective and efficient to construct approximations of sets in dynamic covering information systems. Finally, we preform knowledge reduction of dynamic covering decision information systems by using the incremental approaches.
Vorheriger Artikel Closed-set lattice and modular matroid induced by covering-based rough sets
Nächster Artikel Improving news articles recommendations via user clustering

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Metadaten
Titel
Incremental approaches to knowledge reduction based on characteristic matrices
verfasst von
Guangming Lang
Qingguo Li
Mingjie Cai
Tian Yang
Qimei Xiao
Publikationsdatum
26.11.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2017
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0315-4

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