An optimized classification algorithm by BP neural network based on PLS and HCA

Due to some correlative or repetitive factors between features or samples with high dimension and large amount of sample data, when traditional back-propagation (BP) neural network is used to solve this classification problem, it will present a series of problems such as network structural redundancy, low learning efficiency, occupation of storage space, consumption of computing time, and so on. All of these problems will restrict the operating efficiency and classification precision of neural network. To avoid them, partial least squares (PLS) algorithm is used to reduce the feature dimension of original data into low-dimensional data as the input of BP neural network, so that it can simplify the structure and accelerate convergence, thus improving the training speed and operating efficiency. In order to improve the classification precision of BP neural network by using hierarchical cluster analysis (HCA), similar samples are put into a sub-class, and some different sub-classes can be obtained. For each sub-class, a different training session can be conducted to find a corresponding precision BP neural network model, and the simulation samples of different sub-classes can be recognized by the corresponding network model. In this paper, the theories of PLS and HCA are combined together with the property of BP neural network, and an optimized classification algorithm by BP neural network based on PLS and HCA (PLS-HCA-BP algorithm) is proposed. The new algorithm is aimed at improving the operating efficiency and classification precision so as to provide a more reliable and more convenient tool for complex pattern classification systems. Three experiments and comparisons with four other algorithms are carried out to verify the superiority of the proposed algorithm, and the results indicate a good picture of the PLS-HCA-BP algorithm, which is worthy of further promotion.