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文章基本信息

  • 标题:Decision Tree Creation Methodology Using Propositionalized Attributes
  • 本地全文:下载
  • 作者:Pēteris Grabusts ; Arkādijs Borisovs ; Ludmila Aleksejeva
  • 期刊名称:Information Technology and Management Science
  • 印刷版ISSN:2255-9086
  • 电子版ISSN:2255-9094
  • 出版年度:2016
  • 卷号:19
  • 期号:1
  • 页码:34-38
  • DOI:10.1515/itms-2016-0008
  • 语种:English
  • 出版社:Walter de Gruyter GmbH
  • 摘要:The aim of the article is to analyse and thoroughly research the methods of construction of the decision trees that use decision tree learning with statement propositionalized attributes. Classical decision tree learning algorithms, as well as decision tree learning with propositionalized attributes have been observed. The article provides the detailed analysis of one of the methodologies on the importance of using the decision trees in knowledge presentation. The concept of ontology use is offered to develop classification systems of decision trees. The application of the methodology would allow improving the classification accuracy.
  • 关键词:Decision tree ; ontology ; propositionalization ; taxonomy
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