The week's pick
Reseach Article
Study of Various Decision Tree Pruning Methods with their Empirical Comparison in WEKA
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 60 - Number 12 |
| Year of Publication: 2012 |
| Authors: Nikita Patel, Saurabh Upadhyay |
10.5120/9744-4304
|
Nikita Patel, Saurabh Upadhyay . Study of Various Decision Tree Pruning Methods with their Empirical Comparison in WEKA. International Journal of Computer Applications. 60, 12 ( December 2012), 20-25. DOI=10.5120/9744-4304
Abstract
Classification is important problem in data mining. Given a data set, classifier generates meaningful description for each class. Decision trees are most effective and widely used classification methods. There are several algorithms for induction of decision trees. These trees are first induced and then prune subtrees with subsequent pruning phase to improve accuracy and prevent overfitting. In this paper, various pruning methods are discussed with their features and also effectiveness of pruning is evaluated. Accuracy is measured for diabetes and glass dataset with various pruning factors. The experiments are shown for this two datasets for measuring accuracy and size of the tree.
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