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Study of Various Decision Tree Pruning Methods with their Empirical Comparison in WEKA

by Nikita Patel, Saurabh Upadhyay
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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

@article{ 10.5120/9744-4304,
author = { Nikita Patel, Saurabh Upadhyay },
title = { Study of Various Decision Tree Pruning Methods with their Empirical Comparison in WEKA },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 60 },
number = { 12 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 20-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume60/number12/9744-4304/ },
doi = { 10.5120/9744-4304 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:07:50.389687+05:30
%A Nikita Patel
%A Saurabh Upadhyay
%T Study of Various Decision Tree Pruning Methods with their Empirical Comparison in WEKA
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 12
%P 20-25
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
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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Index Terms

Computer Science
Information Sciences

Keywords

Attribute Selection Measures Decision tree Post pruning Pre pruning

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