基于因果发现的神经网络集成方法

现有的神经网络集成方法主要通过扰动训练数据来产生精确且差异度较大的个体网络,从而获得较强的泛化能力.利用因果发现技术,在取样结果中找出类别属性的祖先属性,然后使用仅包含这些属性的数据生成个体网络,从而有效地将扰动训练数据与扰动输入属性结合起来,以产生精确度高且差异度大的个体.实验结果表明,该方法的泛化能力与当前一些流行的集成方法相当或更好.

Causal Discovery Based Neural Network Ensemble Method

Current neural network ensemble methods usually generate accurate and diverse component networks by disturbing the training data, and therefore achieve strong generalization ability. In this paper, causal discovery is employed to discover the ancestor attributes of the class attribute on the results of the sampling process. Then, component neural networks are trained on the samples with only the ancestor attributes being used as inputs. Thus, the mechanism of disturbing the training data and the input attribute is combined to help generate accurate and diverse component networks. Experiments show that the generalization ability of the proposed method is better than or comparable to that of the ensembles generated by some prevailing methods.