Hierarchical Neural Network Ensemble Modeling Based on Parallel and Serial Structure

Hierarchical models have superiority in solving the data dimension explosion problem. An appropriate hierarchical structure is an important factor affecting the robustness of model. A hierarchical structure is constructed, which combines the parallel learning and serial connection manner. Meanwhile, a hierarchical neural network based on parallel and serial structure (PSHNN) is proposed. Firstly, the hierarchical clustering method is used to determine feasible divisions of the data. In this way, the data can be represented as a set of several subsets. These subsets are input into the serial hierarchical structure with layer by layer manner. Each serial hierarchical structure is constructed by several sublayers. These sublayers are connected via serial manner. Secondly, several parallel layers are established to perform the parallel learning, and each parallel layer contains a serial hierarchical structure. Each parallel layer can be considered as a parallel learning channel. Finally, the Bayesian information criterion (BIC) is used to integrate the learning results of each parallel channel, and determine final output of model. Performance of PSHNN was verified on several benchmark datasets, and an actual collected fatty liver medical dataset. Simulation results prove the superiority of the designed PSHNN.