Polish (Poland)English (United Kingdom)

A New Method of Improving Classification Accuracy of Decision Tree in Case of Incomplete Samples

Type of Publication: In Proceedings
Year: 2013
Authors:
Editor: Rutkowski, Leszek and Korytkowski, Marcin and Scherer, Rafał and Tadeusiewicz, Ryszard and Zadeh, LotfiA. and Zurada, JacekM. Volume: 7894
Book title: Artificial Intelligence and Soft Computing
Series: Lecture Notes in Computer Science Pages: 448-458
ISBN: 978-3-642-38657-2
BibTex:
Abstract:
In the paper a new method is proposed which improves the classification accuracy of decision trees for samples with missing values. This aim was achieved by adding new nodes to the decision tree. The proposed procedure applies structures and functions of well-known C4.5 algorithm. However, it can be easily adapted to other methods, for forming decision trees. The efficiency of the new algorithm has been confirmed by tests using eleven databases from UCI Repository. The research has been concerned classification but the method is not limited to classification tasks.

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