Research On Feature Selection Based On BP Neural Networks

Data mining is a technology which can abstract useful information from large-scale dataset, data pre-processing is an important link of data mining process, especially in high dimensional data mining. Usually, the data used for data analysis may contain hundreds of features, and many of them are not relevant to data mining. Therefore, it is particularly important to find out the minimum set of features to effectively improve the efficiency of data mining.There are many data mining tools of classification, one of which is neural networks, and BP neural networks used in data mining of classification most commonly. However, there are many defects exist in the methods of neural networks feature selection, because the efficiency of learning algorithm of neural networks is not too high itself, and if we adopt the total features of dataset to train a neural networks, the scale of the network will be very large, the information of the network will be very huge, the studying and predicting efficiency of the network will be bad. In order to overcome the deficiencies of neural networks feature selection, we need to propose a new approach to improve the existing methods.An improved neural networks features selection method is presented in the paper, it combines the advantages of Wrapper model and Filter model, and this approach can improve the defects of BP neural networks, which speed up the prediction efficiency of BP neural networks and enhances the prediction accuracy of networks. It ranks the initial features set by using the method of sensitivity analysis, and then we removes the secondary features according to the features ranking results to compare the accuracy of BP neural network prediction and classification between before and after removing the secondary features, at last we can get the minimum set of feature set by making a comparison of the prediction in different situations .The simulation results which based on the MATLAB tool show the efficiency of this approach.