Integration of bagging and boosting with a new reweighting technique

We propose a novel ensemble learning method, IBB (Integration of Boosting and Bagging). This method creates initial classifiers by bagging, and then builds base classifiers by boosting using the previously created classifiers. IBB has two new techniques, a reweighting technique and data adaptation. The reweighting technique increases a weight of a sample which is misclassified by both the ensemble classifier and previously created base classifier. The data adaptation is realized by controlling the number of iteration in boosting. Experimental results using the datasets of UCI machine learning repository show that IBB resulted better accuracy than the other ensemble learning methods on several datasets and on average.