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

作者: Bartosz A. Nowak , Robert K. Nowicki , Wojciech K. Mleczko

DOI: 10.1007/978-3-642-38658-9_40

关键词:

摘要: In the paper a new method is proposed which improves classification accuracy of decision trees for samples with missing values. This aim was achieved by adding nodes to tree. The procedure applies structures and functions well-known C4.5 algorithm. However, it can be easily adapted other methods, forming trees. efficiency algorithm has been confirmed tests using eleven databases from UCI Repository. research concerned but not limited tasks.

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