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Publikācija: Decision Tree Creation Methodology Using Propositionalized Attributes

Publication Type Publication (anonimusly reviewed) in a journal with an international editorial board indexed in other databases
Funding for basic activity Unknown
Defending: ,
Publication language English (en)
Title in original language Decision Tree Creation Methodology Using Propositionalized Attributes
Field of research 2. Engineering and technology
Sub-field of research 2.2 Electrical engineering, Electronic engineering, Information and communication engineering
Authors Pēteris Grabusts
Arkādijs Borisovs
Ludmila Aleksejeva
Keywords 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.
Abstract 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.
DOI: 10.1515/itms-2016-0008
Reference Grabusts, P., Borisovs, A., Aleksejeva, L. Decision Tree Creation Methodology Using Propositionalized Attributes. Information Technology and Management Science, 2016, 19, pp.34-38. ISSN 2255-9086. e-ISSN 2255-9094. Available from: doi:10.1515/itms-2016-0008
ID 23719