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| Diffusion logistic regression algorithms over multiagent networks |
| YanDU,LijuanJIA,ShunshokuKANAE,ZijiangYANG |
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| (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) |
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| 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 |
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| 基金项目:This work was supported in part by the National Natural Science foundation of China (No. 41927801). |
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| 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 |
