Designing ANN using sensitivity & hypothesis correlation testing

Now a day artificial neural network (ANN) has become one of the most prominent concepts in the field of artificial intelligence. ANN has already been applied in the thousands of real life applications. In the arena of classification problem ANN is used massively. But the key issue is in almost all situations the performance of it depends on the architecture of the ANN. As a result designing a proper ANN is always a vital issue in the field of neural networks. The determination of an appropriate ANN architecture is always a challenging task for the ANN designers. This paper proposes a pruning algorithm for designing a three layered ANN architectures. It is well known that a three layered ANN can solve any kind of linear and nonlinear problems. The proposed algorithm uses some major mathematical concepts: correlation coefficients, standard deviations, and statistical hypothesis testing scheme for designing the ANNs. For that reason the authors propose the new pruning algorithm, ANN designing by sensitivity and hypothesis correlations testing (SHCT), to determine ANN architectures automatically. The salient features of SHCT are that it uses statistical hypothesis testing scheme, standard deviations, correlation coefficients, merging with proper replacements to design the ANNs. To justify the performances of SHCT it has been tested on a number of benchmark problem datasets such as Australian credit cards, breast cancer, diabetes, heart disease, and thyroid.