Research On Neural Network Ensemble And Its Application To Earthquake Prediction

Generalization ability, efficiency, and convenience are the three major topics in the field of machine learning and its applications. Neural network ensemble is a learning paradigm, in which a collection of a finite number of neural networks is trained for the same task. Recently, it has become a hot topic in the machine learning community because of its high generalization ability. In this paper, several novel neural network ensemble methods were proposed and applied to the field of earthquake prediction on the basis of the following advanced techniques i.e. Design Of Experiment, Rough Set Reducts, Feature Weighting, and Parallel Computing.The architecture of the ensemble and the training parameters of individual neural networks are closely relative to the performance of the ensemble and the convenience of the ensemble creation. This paper firstly employed Design Of Experiment to guide users with little experience of using neural networks to design the ensemble architecture and adjust the training parameters of individual neural networks. At the same time, the nearest-neighbor clustering algorithm was used to create the heterogeneous individuals with different hidden nodes. Secondly, the constructive algorithm and selective algorithm were utilized to make the users without any experience of neural networks expediently and automatically construct the ensemble, and then the convenience of the ensemble creation was improved.Generalization ability is the principle issue in the field of machine learning. Feature selection for ensembles has shown to be an effective strategy for improving the generalization ability of the ensemble. In this paper, we focused on how to select the appropriate feature subsets, and employed an optimized approach based on discernibility matrix for determining rough set reducts. Finally, the ensemble with high generalization ability was build up on the projections of the training set. Feature weighting is the general case of feature selection, which has the potential of performing better than (or at least as well as) feature selection. In this paper, a new self-adaptive genetic algorithm was used to conduct a search for the weight vector, which could optimize the classification accuracy of the individual neural networks to improve the generalization ability of the ensemble.Efficiency is another principle issue of machine learning. This paper proposed a parallelization strategy for the neural network ensemble by using MPI (Message Passing Interface) techniques to reduce the complexity and improve the efficiency of...