Feature selection method using neural network

Feature selection is an important part of most learning algorithms. Feature selection is used to select the most relevant features from the data. By selecting only the relevant features of the data, higher predictive accuracy can be achieved and the computational load of the classification system can be reduced. A simple method for feature selection using feedforward neural networks is presented. The method starts by using one input neuron and adds one input at time until the wanted classification accuracy has been achieved or all attributes have been chosen. The algorithm can also be used with other classification methods. Test results are given and they are promising. Our algorithm reduces the size of the feature space significantly and improves classification accuracy. Tests were performed on commonly used databases. Average classification accuracy, when using selected features, was between 79% and 100% depending on the used dataset.