Optimization of β-decision rules relative to number of misclassifications

Beata Zielosko

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

Abstract

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
Pages345-354
Number of pages10
ISBN (Print)9783642347061
DOIs
StatePublished - 2012

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Optimization of β-decision rules relative to number of misclassifications'. Together they form a unique fingerprint.

Cite this