Classifiers based on optimal decision rules

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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).
Original languageEnglish (US)
Pages (from-to)151-160
Number of pages10
JournalFundamenta Informaticae
Volume127
Issue number1-4
DOIs
StatePublished - Nov 25 2013

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Algebra and Number Theory
  • Theoretical Computer Science
  • Information Systems

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