Decision Tree Creation Methodology Using Propositionalized Attributes

Pēteris Grabusts, Arkady Borisov†, Ludmila Aleksejeva


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.


Decision tree; ontology; propositionalization; taxonomy

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M. G. Matveev, A. S. Sviridov and N. A. Aleynikova, Models and methods of artificial intelligence. Application in Economics, Finansy i statistika, 2008, 447 p. (in Russian).

T. M. Mitchell, Machine learning. McGraw-Hill, 1997, 414 p.

J. R. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.

L. Breiman, J. H. Friedman, R. Olshen and C. J. Stone, Classification and regression trees. Belmont, CA: Wadsworth, 1984.

F. Kharbat and H. El-Ghalayini, “Building Ontology from Knowledge Base Systems,” in Proc. of Int. Arab Conf. on Information Technology, 2011.

R. Kohavi and F. Provost, “Applications of data mining to electronic commerce,” in Data Mining and Knowledge Discovery, vol. 5, issue 1, pp. 5–10, Jan. 2001.

P. Grabusts, A. Borisov and L. Aleksejeva, “Ontology-based classification system development methodology,” Information Technology and Management Science, vol. 18, pp. 129–134, 2015.

T. R. Gruber, “A translation approach to portable ontologies,” Knowledge Acquisition, vol. 5, issue 2, pp. 199–220, 1993.

D. Kang and K. Sohn, “Learning decision trees with taxonomy of propositionalized attributes,” Pattern Recognition, vol. 42, no. 1, pp. 84– 92, 2009.

J. R. Quinlan, “Improved use of continuous attributes in C4.5,” Journal of Artificial Intelligence Research, vol. 4, issue 1, pp. 77–90, 1996.

J. R. Quinlan, “Induction of Decision Trees,” Machine learning, vol. 1, no. 1, pp. 81–106, 1986.

M. Licham, “UCI Machine learning repository: Iris data set,” (2013). [Online]. Available: [Accessed: Sept. 30, 2016.].


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