%0 Journal Article
%T An Empirical Comparison of Boosting and Bagging Algorithms
%A R. Kalaichelvi Chandrahasan
%A Angeline Christobel Y
%A Usha Rani Sridhar
%A Arockiam L
%J International Journal of Computer Science and Information Security
%D 2011
%I LJS Publisher and IJCSIS Press
%X Classification is one of the data mining techniques that analyses a given data set and induces a model for each class based on their features present in the data. Bagging and boosting are heuristic approaches to develop classification models. These techniques generate a diverse ensemble of classifiers by manipulating the training data given to a base learning algorithm. They are very successful in improving the accuracy of some algorithms in artificial and real world datasets. We review the algorithms such as AdaBoost, Bagging, ADTree, and Random Forest in conjunction with the Meta classifier and the Decision Tree classifier. Also we describe a large empirical study by comparing several variants. The algorithms are analyzed on Accuracy, Precision, Error Rate and Execution Time.
%K Data Mining
%K Classification
%K Meta classifier
%K Decision Tree
%U https://sites.google.com/site/ijcsis/vol-9-no-11-nov-2011