Abstract:
Feature selection is a well known technique for data prepossessing with the purpose of removing redundant and irrelevant information with the benefits, among others, of an improved generalization and a decreased curse of dimensionality. This paper investigates an approach based on a trained neural network model, where features are selected by iteratively removing a node in the input layer. This pruning process, comprise a node selection criterion and a subsequent weight correction: after a node elimination, the remaining weights are adjusted in a way that the overall network behaviour do not worsen over the entire training set. The pruning problem is formulated as a system of linear equations solved in a least-squares sense. This method allows the direct evaluation of the performance at each iteration and a stopping condition is also proposed. Finally experimental results are presented in comparison to another feature selection method.
