Input dimensionality reduction for radial basis neural network classification problems using sensitivity measure

The curse of dimensionality is always problematic in pattern classification problems. In this paper, we provide a brief comparison of the major methodologies for reducing input dimensionality and summarize them in three categories: correlation among features, transformation and neural network sensitivity analysis. Furthermore, we propose a novel method for reducing input dimensionality that uses a stochastic RBFNN sensitivity measure. The experimental results are promising for our method of reducing input dimensionality.