Cascading model based back propagation neural network in enabling precise classification

Artificial Neural Network has been widely used in types of tasks which are related to classification, for example image annotation, pattern recognition, trend prediction and so on. There are a number of neural network algorithm theories based on various concepts, however, Back Propagation Neural Network (BPNN) has become the most famous one due to its remarkable function approximation ability. BPNN employs feed forward and back propagation to tune the parameters in neurons, and uses only feed forward to execute the classification. Based on gradient descent mechanism, BPNN can effectively find the mapping relationships between input and output. However, BPNN has met one critical issue. Due to large number of sum and sigmoid operations, BPNN has low efficiency in dealing with large number of instances. To speed up the processing, researchers employed data separation. However, simple data separation in training phase results in classification accuracy loss. Therefore, this paper presents a cascading model based BPNN (CBPNN) which aims at enhancing the classification accuracy of BPNN. The paper further employs multi-threading technique to speed up CBPNN. The experimental results indicate that CBPNN can improve the classification precision whilst maintains satisfied efficiency.