Abstract
Due to the existence of redundant features, the Radial-Basis Function Neural Network (RBFNN) which is trained from a dataset is likely to be huge. Sensitivity analysis technique usually could help to reduce the features by deleting insensitive features. Considering the perturbation of network output as a random variable, this paper defines a new sensitivity formula which is the limit of variance of output perturbation with respect to the input perturbation going to zero. To simplify the sensitivity expression and computation, we prove that the exchange between limit and variance is valid. A formula for computing the new sensitivity of individual features is derived. Numerical simulations show that the new sensitivity definition can be used to remove irrelevant features effectively.
This research work is supported by NSFC (60473045) and Natural Science Foundation of Hebei Province (603137).
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Wang, X., Li, C. (2005). A New Definition of Sensitivity for RBFNN and Its Applications to Feature Reduction. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_12
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DOI: https://doi.org/10.1007/11427391_12
Publisher Name: Springer, Berlin, Heidelberg
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