基于广义回归网络的动态权重回归型神经网络集成方法研究

Dynamically Weighted Ensemble Neural Networks with Generalized Regression Neural Network for Solving Regression Problems

神经网络集成技术能有效地提高神经网络的预测精度和泛化能力,已成为机器学习和神经计算领域的一个研究热点。针对回归分析问题提出了一种动态确定结果合成权重的神经网络集成构造方法,在训练出个体神经网络之后,根据各个体网络在输入空间上对训练样本的预测误差,应用广义回归网络来动态地确定各个体网络在特定输入空间上的权重。实验结果表明,与传统的简单平均和加权平均方法相比,本集成方法能取得更好的预测精度。

Combining the outputs of several neural networks into an aggregate output often gives improved accuracy over any individual output. This paper presents an ensemble method for regression that has advantages over weighted average combining techniques. Generally, the output of an ensemble is a weighted sum which are weights fixed. The ensembles are weighted dynamically, the weights dynamically determined from the predicted accuracies of the trained networks with training dataset, the more accurate a network seems to be of its prediction, the higher the weight. This is implemented by generalized regression neural network. Empirical results show that this method improved on prediction accuracy.