Optimization of Approximate Inhibitory Rules Relative to Number of Misclassifications

Fawaz Alsolami, Igor Chikalov, Mikhail Moshkov, Beata Zielosko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


In this work, we consider so-called nonredundant inhibitory rules, containing an expression “attribute:F value” on the right- hand side, for which the number of misclassifications is at most a threshold γ. We study a dynamic programming approach for description of the considered set of rules. This approach allows also the optimization of nonredundant inhibitory rules relative to the length and coverage. The aim of this paper is to investigate an additional possibility of optimization relative to the number of misclassifications. The results of experiments with decision tables from the UCI Machine Learning Repository show this additional optimization achieves a fewer misclassifications. Thus, the proposed optimization procedure is promising.
Original languageEnglish (US)
Title of host publicationProcedia Computer Science
PublisherElsevier BV
Number of pages8
StatePublished - Oct 4 2013

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


Dive into the research topics of 'Optimization of Approximate Inhibitory Rules Relative to Number of Misclassifications'. Together they form a unique fingerprint.

Cite this