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Diffusion logistic regression algorithms over multiagent networks
YanDU,LijuanJIA,ShunshokuKANAE,ZijiangYANG
0
(School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China;Department of Medical Engineering, Faculty of Health Science, Junshin Gakune University, Fukuoka, Japan;Department of Intelligent Systems Engineering, Ibaraki University, 4-12-1 Nakanarusawa, Hitachi, Ibaraki 316-8511, Japan)
摘要:
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.
关键词:  Logistic regression, bagging, diffusion strategy, connected network
DOI:https://doi.org/10.1007/s11768-020-0009-2
基金项目:This work was supported in part by the National Natural Science foundation of China (No. 41927801).
Diffusion logistic regression algorithms over multiagent networks
Yan DU,Lijuan JIA,Shunshoku KANAE,Zijiang YANG
(School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China;Department of Medical Engineering, Faculty of Health Science, Junshin Gakune University, Fukuoka, Japan;Department of Intelligent Systems Engineering, Ibaraki University, 4-12-1 Nakanarusawa, Hitachi, Ibaraki 316-8511, Japan)
Abstract:
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.
Key words:  Logistic regression, bagging, diffusion strategy, connected network