Using Boosting and Clustering to Prune Bagging and Detect Noisy Data

摘要

AdaBoost has been the representation of ensemble learning algorithm because of its excellent performance. However, due to its longtime training, AdaBoost was complained about by people and this defect limits the practical application. Bagging is a rapid method of training and supports for parallel computing. One of important factors that can affect the performance of ensemble learning is the diversity of component learners. Based on this view, a new algorithm using clustering and boosting to prune Bagging ensembles is proposed in this paper. Its learning efficiency is close to Bagging and its performance is close to AdaBoost. Furthermore, this new algorithm can detect noisy data from original samples based on cascade technique, and a better result of noise detection can be acquired.