Improved Post Pruning of Decision Trees |
Author(s): |
| Roopa.C , KCG College of Technology; A. Thamaraiselvi, KCG College of Technology; S. Preethi Lakshmi, KCG College of Technology; N. Bhaskar , KCG College of Technology |
Keywords: |
| Clustering, Decision Tree, Pruning, Data Mining |
Abstract |
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Decision trees are strong predictors which are used to explicitly represent large data sets. An efficient pruning method will prune or eliminate the non-predictive parts of the model and generate a small and accurate model. This paper presents an overview of the issues present in decision trees and the pruning techniques. We evaluated the results of our pruning method on a variety of machine learning data sets from UCI machine learning repository and found that it generates a concise and accurate model. |
Other Details |
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Paper ID: IJSRDV3I21035 Published in: Volume : 3, Issue : 2 Publication Date: 01/05/2015 Page(s): 2413-2417 |
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