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In the industrial field, the artificial neural network classifiers are currently used and they are generally integrated of technologic systems which need efficient classifier. Statistical classifiers also have been developed in the same direction and different associations and optimization procedures have been proposed as Adaboost training or CART algorithm to improve the classification performance. However, the objective comparison studies between these novel classifiers stay marginal. In the present work, we intend to evaluate with a new criterion the classification stability between neural networks and some statistical classifiers based on the optimization Fischer criterion or the maximization of Patrick-Fischer distance orthogonal estimator. The stability comparison is performed by the error rate probability densities estimation which is valorised by the performed kernel-diffeomorphism Plug-in algorithm. The results obtained show that the statistical approaches are more stable compared to the neural networks.

Comparison of Statistical and Artificial Neural Networks Classifiers by Adjusted Non Parametric Probability Density Function Estimate