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    李 凯, 黄厚宽. 一种基于聚类技术的选择性神经网络集成方法[J]. 计算机研究与发展, 2005, 42(4): 594-598.
    引用本文: 李 凯, 黄厚宽. 一种基于聚类技术的选择性神经网络集成方法[J]. 计算机研究与发展, 2005, 42(4): 594-598.
    Li Kai, Huang Houkuan. A Selective Approach to Neural Network Ensemble Based on Clustering Technology[J]. Journal of Computer Research and Development, 2005, 42(4): 594-598.
    Citation: Li Kai, Huang Houkuan. A Selective Approach to Neural Network Ensemble Based on Clustering Technology[J]. Journal of Computer Research and Development, 2005, 42(4): 594-598.

    一种基于聚类技术的选择性神经网络集成方法

    A Selective Approach to Neural Network Ensemble Based on Clustering Technology

    • 摘要: 神经网络集成是一种很流行的学习方法,通过组合每个神经网络的输出生成最后的预测.为 了提高集成方法的有效性,不仅要求集成中的个体神经网络具有很高的正确率,而且要求这 些网络在输入空间产生不相关的错误.然而,在现有的众多集成方法中,大都采用将训练的 所有神经网络直接进行组合以形成集成,实际上生成的这些神经网络可能具有一定的相关性 .为了进一步提高神经网络间的差异性,一种基于聚类技术的选择性神经网络集成方法CLU_E NN被提出.在获得个体神经网络后,并不直接对这些神经网络集成,而是先应用聚类算法对 这些神经网络模型聚类以获得差异较大的部分神经网络;然后由部分神经网络构成集成;最 后,通过实验研究了CLU_ENN集成方法,与传统的集成方法Bagging相比,该方法取得了更好 的效果.

       

      Abstract: A neural network ensemble is a very popular learning paradigm where the outputs of a set of separately trained neural network are combined to form one unified p rediction. To improve the effectiveness of ensemble, neural networks in the ense mble are not only highly correct but make their errors on different parts of the input space as well. However, most existing approaches ensemble all the availab le neural networks for prediction. In this paper, a selective approach to neural network ensemble based on clustering technology is presented. After individual neural networks are trained, the clustering algorithm is used to select a part o f the trained individual networks in order to reduce their similarity. Then many selected neural networks are combined. Experimental results show that this appr oach outperforms the traditional ones that ensemble all of the individual networ ks.

       

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