A new hybrid expansion function based mutual information for a multilayer neural networks optimization

Kais NCIBI; Amor DJENINA; Tarek SADRAOUI; Faycel MILI

doi:10.54960/jfcg.v1i2.6

Authors

Kais NCIBI University of economics and Management of Sfax MODELIS Lab
Amor DJENINA University of Tebessa Algeria
Tarek SADRAOUI University of economics and Management of Mahdia, MODELIS Lab. Tunisia.
Faycel MILI University of economics and Management of Mahdia, MODELIS Lab. Tunisia

DOI:

https://doi.org/10.54960/jfcg.v1i2.6

Keywords:

Function expansion, multilayer, perceptron, Classification, mutual information, features selection

Abstract

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

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