Optimization of partial association rules relative to number of misclassifications

Beata Zielosko, Marek Robaszkiewicz

Abstract


In the paper, an optimization of partial association rules relative to number of misclassifications is presented. The aims of proposed optimization are: (i) construction of rules with small number of misclassifications, what is important from the point of view of construction of classifiers, (ii) decreasing the number of rules, what is important from the point of view of knowledge representation. The paper contains experimental results for data sets from UCI Machine Learning Repository.

Keywords


rough sets; greedy algorithm; partial association rules; misclassifications

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DOI: http://dx.doi.org/10.21936/si2016_v37.n1.757