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

06.08.2016 | Original Article

Method for generating decision implication canonical basis based on true premises

verfasst von: Deyu Li, Shaoxia Zhang, Yanhui Zhai

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

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Abstract

Formal concept analysis is able to visualize and represent knowledge using concept lattice and (attribute) implication. Decision implication is a counterpart of implication in the setting of decision-making. Decision implication canonical basis is a complete, non-redundant and optimal set of decision implications. At present, decision implication canonical basis can be generated with the help of minimal generators; however, this method is not efficient because of its exponential complexity. To solve this problem, we propose an algorithm to generate decision implication canonical basis based on true premises and analyze its time complexity. Experimental results verify the efficiency of this algorithm.
Vorheriger Artikel Multi-level interval-valued fuzzy concept lattices and their attribute reduction
Nächster Artikel Three-way fuzzy concept lattice representation using neutrosophic set

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Metadaten
Titel
Method for generating decision implication canonical basis based on true premises
verfasst von
Deyu Li
Shaoxia Zhang
Yanhui Zhai
Publikationsdatum
06.08.2016
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/s13042-016-0575-2

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