Decision Tree Creation Methodology Using Propositionalized Attributes
Peter Grabusts, Arkādijs Borisovs, Ludmila Aleksejeva · Information Technology and Management Science · 2016
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.