神经网络分类器动态集成方法

提出一种神经网络分类器的动态集成方法.基于bootstrapping构建不同的个体神经网络,针对混合属性,通过不同的加权最近邻设计评估单个网络的分类精度,在此基础上动态选择误差率较小的神经网络,经过投票形成集成分类结果.将该方法与其它几种集成方法在10个UCI数据集上进行了分类性能比较.实验结果表明,该方法在上述所有数据集上的平均分类精度最佳,同时发现,Bagging比隐层神经元数法能更好地生成个体网络,而将两者结合起来训练个体神经网络,并不能明显提高集成性能.

Dynamic Integration Approach for an Ensemble of Neural Classifiers

A dynamic integration approach for an ensemble of neural classifiers(NCs) was presented in this paper.It established different NCs based on bootstrapping technique,and evaluated the classification accuracy of every NC by different sorts of weighted nearest neighbors for mixed attributes,then the NCs with low relative generalization error rates were dynamically selected and majority voting was applied to those NCs in order to conduct the final classification results of the ensemble.This approach was compared with some integration approaches on classification performance for ten data sets from UCI.The experiments showed that this approach could obtain the best average classification accuracy over all those data sets.At the same time,it is easy to see that Bagging is better than the method with different number of hidden units(MDHU) for generating different NCs, and the performance of the ensemble may not be improved by combining Bagging with MDHU.