A Classifier Using Online Bagging Ensemble Method for Big Data Stream Learning

A Classifier Using Online Bagging Ensemble

Method for Big Data Stream Learning

Yanxia Lv;Sancheng Peng;Ying Yuan;Cong Wang;Pengfei Yin;Jiemin Liu;Cuirong Wang

【期刊名称】《清华大学学报（英文版）》 【年(卷),期】2019(024)004

【摘要】By combining multiple weak learners with concept drift in the classification of big data stream learning,the ensemble learning can achieve better generalization performance than the single learning approach.In this paper,we present an efficient classifier using the online bagging ensemble method for big data stream learning.In this classifier,we introduce an efficient online resampling mechanism on the training instances,and use a robust coding method based on error-correcting output codes.This is done in order to reduce the effects of correlations between the classifiers and increase the diversity of the ensemble.A dynamic updating model based on classification performance is adopted to reduce the unnecessary updating operations and improve the efficiency of learning.We implement a parallel version of EoBag,which runs faster than the serial version,and results indicate that the classification performance is almost the same as the serial one.Finally,we compare the performance of classification and the usage of resources with other state-of-the-art algorithms using the artificial and the actual data sets,respectively.Results show that the proposed