A Multilayer Perceptron Artificial Neural Networks Based a Preprocessing and Hybrid Optimization Task for Data Mining and Classification

Artificial neural networks (ANNs) optimization represent an attractive area that attract many researchers in different disciplines, this in the aim to improve the performance of this model. In literature, there is no fix theory that illustrates how to construct this non linear model. Thus, all proposed construction was based on empirical illustration. Multilayer perceptron (MLP) is one of the most used models in ANNs area. It was described as a good non linear approximator with a power ability to lean well non linear system, and most of research was limited to a 3 layers MLP, by describing that 3 layers are sufficient to have good approximation. In this context we are interested to this model construction for solving supervised classification tasks in data mining. This construction requires a preprocessing phase that seems to scribe be important for the final performance. This paper present a process of MLP construction based on two phases: a preparation phase and an optimization phase. The first one describes a process of data cleaning, discretization, normalization, expansion, reduction and features selection. The second phase aims to optimize the set of weights based on some combination of hybrid algorithms such back-propagation algorithm, a local search and different evolution. An empirical illustration will be done to in order to validate the proposed model. At the end, a comparison with others known classifiers will be done to justify the validity of the proposed model.