TY  - JOUR
T1  - A Comparative Study of Bagging, Boosting and C4.5: The Recent Improvements in Decision Tree Learning Algorithm
AU - Huda, Nazmul AU - Kumar Pramanik, Bimal AU - Nanda Chowdhury, Utpala AU - Pramanik, Subrata 
JO  - Asian Journal of Information Technology
VL  - 9
IS  - 6
SP  - 300
EP  - 306
PY  - 2010
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2010.300.306
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2010.300.306
KW  - C4.5
KW  -ID3
KW  -decision tree induction
KW  -decision tree
KW  -Data mining
KW  -bagging
KW  -boosting
AB  - Decision tree learning algorithm has been successfully used in expert systems in capturing knowledge and presents a powerful method of inferring classification rules from a set of labeled examples. ID3 is a well known and the most basic decision tree-learning algorithm that is based on information gain theory. Improvements are made to this decision tree induction algorithm by Quinlan’s C4.5 algorithm that uses gain ratio as opposed to information gain. Breiman’s Bagging and Freund and Schapire’s Boosting are recent methods of improving the predictive power of any classifier learning system. Both form a set of classifiers that are combined by voting, bagging by generating samples with replication of the data and boosting by adjusting the weights of training instances. In this research work both bagging and boosting have been applied to C4.5 algorithm and the corresponding predictive accuracies are computed by testing on a representative dataset. While both approaches substantially improve predictive accuracy of C4.5, boosting shows the greater benefit.
ER  -