Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem

Artificial neural networks have been shown to perform well for two-group classification problems. However, current research has yet to determine a method for identifying relevant input variables in the neural network model for real world classification problems. The common practice in neural network research is to include all available input variables that could possibly contribute to the model without determination of whether they help in estimating the unknown function. One problem with this avenue of neural network research is the inability to extract the knowledge that could be useful to researchers by identifying those input variables that contribute to estimating the true underlying function of the data. A method has been proposed in past research, the Neural Network Simultaneous Optimization Algorithm (NNSOA), which was shown to be successful for a limited number of continuous problems. This research proposes using the NNSOA on a real world classification problem that not only finds good solutions for estimating unknown functions, but can also correctly identify those variables that contribute to the model.