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Annals of computer science and information systems
Article . 2024 . Peer-reviewed
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https://dx.doi.org/10.48550/ar...
Article . 2024
License: arXiv Non-Exclusive Distribution
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Preprint . 2024
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Conference object . 2025
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Lower Bounds on Cardinality of Reducts for Decision Tables from Closed Classes

Authors: Azimkhon Ostonov; Mikhail Moshkov;

Lower Bounds on Cardinality of Reducts for Decision Tables from Closed Classes

Abstract

In this paper, we consider classes of decision tables closed under removal of attributes (columns) and changing of decisions attached to rows. For decision tables from closed classes, we study lower bounds on the minimum cardinality of reducts, which are minimal sets of attributes that allow us to recognize, for a given row, the decision attached to it. We assume that the number of rows in decision tables from the closed class is not bounded from above by a constant. We divide the set of such closed classes into two families. In one family, only standard lower bounds $Ω(\log $ ${\rm cl}(T))$ on the minimum cardinality of reducts for decision tables hold, where ${\rm cl}(T)$ is the number of decision classes in the table $T$. In another family, these bounds can be essentially tightened up to $Ω({\rm cl}(T)^{1/q})$ for some natural $q$.

Country
Saudi Arabia
Keywords

FOS: Computer and information sciences, Computer Science - Computational Complexity, Electronic computers. Computer science, 511, Information technology, QA75.5-76.95, Computational Complexity (cs.CC), T58.5-58.64

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
Published in a Diamond OA journal