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

Azimkhon Ostonov*, Mikhail Moshkov

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

Abstract

In this research paper, we examine classes of decision tables that are closed under attribute (column) removal and changing of decisions associated with rows. For decision tables belonging to these closed classes, we investigate lower bounds on the minimum cardinality of reducts. Reducts are minimal sets of attributes that allow us to determine the decision attached to a given row. We assume that the number of rows in the decision tables from the closed class is not limited by a constant. We divide the set of these closed classes into two families. In one family, the minimum cardinality of reducts for decision tables is bounded by standard lower bounds of the form Ω(logcl(T)), where cl(T) represents the number of decision classes in the table T. In the other family, these lower bounds can be significantly tightened to the form Ω(cl(T)1/q) for some natural number q.

Original languageEnglish (US)
Pages667-670
Number of pages4
DOIs
StatePublished - 2024
Event19th Conference on Computer Science and Intelligence Systems, FedCSIS 2024 - Belgrade, Serbia
Duration: Sep 8 2024Sep 11 2024

Conference

Conference19th Conference on Computer Science and Intelligence Systems, FedCSIS 2024
Country/TerritorySerbia
CityBelgrade
Period09/8/2409/11/24

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management

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