Feature selection using Sequential Forward Selection and classification applying Artificial Metaplasticity Neural Network

The feature selection has been widely used to reduce the data dimensionality. Data reduction improve the classification performance, the approximation function, and pattern recognition systems in terms of speed, accuracy and simplicity. A strategy to reduce the number of features in local search are the sequential search algorithms. In this work is presented a feature selection method based on Sequential Forward Selection (SFS) and Feed Forward Neural Network (FFNN) to estimate the prediction error as a selection criterion. Three well-known database have been used to test the SFS-FFNN with Artificial Metaplasticity on Perceptron Multilayer (AMMLP). The AMMLP is a new method applied for classification of patterns. The results obtained by SFS-FFNN with AMMLP in classification accuracy are superior than obtained by conventional BP algorithm and other recent feature selection algorithms applied to the same database. By these reasons the proposed method SFS-FFNN with AMMLP is an interesting alternative to reduce the data dimensionality and provide a high accuracy.