Optimization of approximate decision rules relative to number of misclassifications: Comparison of greedy and dynamic programming approaches

Talha M. Amin, Igor Chikalov, Mikhail Moshkov, Beata Zielosko

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

2 Scopus citations

Abstract

In the paper, we present a comparison of dynamic programming and greedy approaches for construction and optimization of approximate decision rules relative to the number of misclassifications. We use an uncertainty measure that is a difference between the number of rows in a decision table T and the number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules that localize rows in subtables of T with uncertainty at most γ. Experimental results with decision tables from the UCI Machine Learning Repository are also presented. © 2013 Springer-Verlag.
Original languageEnglish (US)
Title of host publicationKnowledge Engineering, Machine Learning and Lattice Computing with Applications
PublisherSpringer Nature
Pages41-50
Number of pages10
ISBN (Print)9783642373428
DOIs
StatePublished - 2013

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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