Input sample selection for RBF neural network classification problems using sensitivity measure

Large data sets containing irrelevant or redundant input samples reduce the performance of learning and increases storage and labeling costs. This work compares several sample selection and active learning techniques and proposes a novel sample selection method based on the stochastic radial basis function neural network sensitivity measure (SM). The experimental results for the UCI IRIS data set show that we can remove 99% of data while keeping 95% of classification accuracy when applying both sensitivity based feature and sample selection methods. We propose a single and consistent method, which is robust enough to handle both feature and sample selection for a supervised RBFNN classification system, by using the same neural network architecture for both selection and classification tasks.