Optimization of β-decision rules relative to number of misclassifications

Beata Zielosko

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


In the paper, we present an algorithm for optimization of approximate decision rules relative to the number of misclassifications. The considered algorithm is based on extensions of dynamic programming and constructs a directed acyclic graph Δ β (T). Based on this graph we can describe the whole set of so-called irredundant β-decision rules. We can optimize rules from this set according to the number of misclassifications. Results of experiments with decision tables from the UCI Machine Learning Repository are presented. © 2012 Springer-Verlag.
Original languageEnglish (US)
Title of host publicationComputational Collective Intelligence. Technologies and Applications
PublisherSpringer Nature
Number of pages10
ISBN (Print)9783642347061
StatePublished - 2012

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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