Bagging and Boosting with Dynamic Integration of Classifiers

One approach in classification tasks is to use machine learning techniques to derive classifiers using learning instances. The cooperation of several base classifiers as a decision committee has succeeded to reduce classification error. The main current decision committee learning approaches boosting and bagging use resampling with the training set and they can be used with different machine learning techniques which derive base classifiers. Boosting uses a kind of weighted voting and bagging uses equal weight voting as a combining method. Both do not take into account the local aspects that the base classifiers may have inside the problem space. We have proposed a dynamic integration technique to be used with ensembles of classifiers. In this paper, the proposed dynamic integration technique is applied with AdaBoost and bagging. The comparison results using several datasets of the UCI machine learning repository show that boosting and bagging with dynamic integration of classifiers results often better accuracy than boosting and bagging result with their original voting techniques.