On algorithm for building of optimal α-decision trees

Abdulaziz Alkhalid, Igor Chikalov, Mikhail Moshkov

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

22 Scopus citations


The paper describes an algorithm that constructs approximate decision trees (α-decision trees), which are optimal relatively to one of the following complexity measures: depth, total path length or number of nodes. The algorithm uses dynamic programming and extends methods described in [4] to constructing approximate decision trees. Adjustable approximation rate allows controlling algorithm complexity. The algorithm is applied to build optimal α-decision trees for two data sets from UCI Machine Learning Repository [1]. © 2010 Springer-Verlag Berlin Heidelberg.
Original languageEnglish (US)
Title of host publicationRough Sets and Current Trends in Computing
PublisherSpringer Nature
Number of pages8
ISBN (Print)3642135285; 9783642135286
StatePublished - 2010

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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