一种结构自适应的神经网络特征选择方法

特征选择是数据处理的一项重要内容，现有的基于神经网络的特征选择方法没有考虑网络中隐结点数目的变化，使网络结构的特征选择过程中往往变得不合理，这阻碍了特征的进一步删除以及网络泛化性能的提高，针对以上问题提出了一种结构自适应的神经网络特征选择方法，通过交替删除网络中冗余的输入特征和隐结点，使网络结构在特征选择的过程中保持相对良好，实验表明，该方法既能快速有效地删除特征，又提高了网络的泛化性能。

A STRUCTURE-ADAPTIVE APPROACH FOR NEURAL-NETWORK-BASED FEATURE SELECTION

Feature selection is an important part of data processing. Conventional approaches ignore the change of number of hidden units, so network architecture often becomes irrational in the process of feature selection. It hinders further selection of features and further improvement of network generation ability. To solve the above problem, a structure adaptive approach for neural network based feature selection is proposed in this paper. By pruning the redundant input features and hidden units alternatively, network architecture is kept reasonable. Experiments show that this method can effectively select features while improving the generalization ability of network.