Sequential optimization of approximate inhibitory rules relative to the length, coverage and number of misclassifications

Fawaz Alsolami, Igor Chikalov, Mikhail Moshkov

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

2 Scopus citations

Abstract

This paper is devoted to the study of algorithms for sequential optimization of approximate inhibitory rules relative to the length, coverage and number of misclassifications. Theses algorithms are based on extensions of dynamic programming approach. The results of experiments for decision tables from UCI Machine Learning Repository are discussed. © 2013 Springer-Verlag.
Original languageEnglish (US)
Title of host publicationRough Sets and Knowledge Technology
PublisherSpringer Nature
Pages154-165
Number of pages12
ISBN (Print)9783642412981
DOIs
StatePublished - 2013

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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