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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 1/2020

30-04-2019 | Original Article

Local attribute reductions of formal contexts

Authors: Keyun Qin, Hong Lin, Yuting Jiang

Published in: International Journal of Machine Learning and Cybernetics | Issue 1/2020

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Abstract

Attribute reductions is an important topic in formal concept analysis. The existing attribute reduction approaches are dominated by global reductions and there is very limited investigation on local reductions. This paper is devoted to the study of decision rule specific reduction for formal decision context and concept specific reduction for formal context. The notion of decision rule specific reduction is proposed and the related reduction methods are presented. The relationships between existing reduction approaches and decision rule specific reduction approaches are analyzed. Accordingly, we make an analysis of attributes based on three-way classification by using reductions. Furthermore, the notion of concept specific reduction for formal context is proposed and the concept specific reduction methods are examined. The relationship between concept specific reduction and decision rule specific reduction is surveyed.
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Metadata
Title
Local attribute reductions of formal contexts
Authors
Keyun Qin
Hong Lin
Yuting Jiang
Publication date
30-04-2019
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 1/2020
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-019-00956-z

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