In this paper,a distributed scheme is proposed for ensemble learning method of bagging,which aims to address the classification problems for large dataset by developing a group of cooperative logistic regression learn...In this paper,a distributed scheme is proposed for ensemble learning method of bagging,which aims to address the classification problems for large dataset by developing a group of cooperative logistic regression learners in a connected network.Moveover,each weak learner/agent can share the local weight vector with its immediate neighbors through diffusion strategy in a fully distributed manner Our diffusion logistic regression algorithms can effectively avoid overfitting and obtain high classification accuracy compared to the non-cooperation mode.Furthermore,simulations with a real dataset are given to demonstrate the effectiveness of the proposed methods in comparison with the centralized one.展开更多
基金supported in part by the National Natural Science foundation of China(No.41927801).
文摘In this paper,a distributed scheme is proposed for ensemble learning method of bagging,which aims to address the classification problems for large dataset by developing a group of cooperative logistic regression learners in a connected network.Moveover,each weak learner/agent can share the local weight vector with its immediate neighbors through diffusion strategy in a fully distributed manner Our diffusion logistic regression algorithms can effectively avoid overfitting and obtain high classification accuracy compared to the non-cooperation mode.Furthermore,simulations with a real dataset are given to demonstrate the effectiveness of the proposed methods in comparison with the centralized one.
