S. B. Kotsiantis and P. E. Pintelas
Combining Bagging and Boosting
372 - 381
2007
1
8
International Journal of Mathematical and Computational Sciences
https://publications.waset.org/pdf/4742
https://publications.waset.org/vol/8
World Academy of Science, Engineering and Technology
Bagging and boosting are among the most popular resampling ensemble methods that generate and combine a diversity of classifiers using the same learning algorithm for the baseclassifiers. Boosting algorithms are considered stronger than bagging on noisefree data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, in this work we built an ensemble using a voting methodology of bagging and boosting ensembles with 10 subclassifiers in each one. We performed a comparison with simple bagging and boosting ensembles with 25 subclassifiers, as well as other well known combining methods, on standard benchmark datasets and the proposed technique was the most accurate.
Open Science Index 8, 2007