• DocumentCode
    2705118
  • Title

    GA Tree: genetically evolved decision trees

  • Author

    Papagelis, Athanassios ; Kalles, Dimitrios

  • Author_Institution
    Comput. Technol. Inst., Patras, Greece
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    203
  • Lastpage
    206
  • Abstract
    We use genetic algorithms to evolve classification decision trees. The performance of the system is measured on a set of standard discretized concept learning problems and compared (very favorably) with the performance of two known algorithms (C4.5, OneR)
  • Keywords
    decision trees; genetic algorithms; learning (artificial intelligence); search problems; C4 5; GA Tree; OneR; classification decision tree evolution; genetic algorithms; genetically evolved decision trees; standard discretized concept learning problems; system performance; Current measurement; Decision trees; Gain measurement; Genetic algorithms; Impurities; Induction generators; Machine learning; Machine learning algorithms; Measurement standards; Medical diagnostic imaging;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence, 2000. ICTAI 2000. Proceedings. 12th IEEE International Conference on
  • Conference_Location
    Vancouver, BC
  • ISSN
    1082-3409
  • Print_ISBN
    0-7695-0909-6
  • Type

    conf

  • DOI
    10.1109/TAI.2000.889871
  • Filename
    889871
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2705118