A new hybrid expansion function based mutual information for a multilayer neural networks optimization
Kais Ncibi, Amor Djenina, Tarek Sadraoui, Faycel MILI · Journal of Finance & Corporate Governance · 2017
Function expansion was used to expand initial features based on a non linear transformation. Many known expansion functions are found such the trigonometric, the polynomial, the Legendre polynomial, the power series, the exponential and the logarithmic transformation. This paper present a comparison between different expansion functions based on mutual information and different performance functions. We propose a new expansion process able to improve the correspondent mutual information and the final performance. The process was tested; using different benchmark databases, and shows his ability to improve results of classification problems