Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS)

DOI: http://dx.doi.org/10.15439/2024F8221

Citation: Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 39, pages 667–670 (2024)

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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 $\Omega(\log {\rm cl}(T))$, where ${\rm cl}(T)$ represents the number of decision classes in the table $T$. In the other family, these lower bounds can be significantly tightened to the form $\Omega({\rm cl}(T)^{1/q})$ for some natural number $q$.

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