Algorithm Design And Analysis On Neural Network Ensemble

Neural network ensemble is a quite active and hot research topic in machine learning and neural computing fields, the research based on this topic is not only helps the scientists make deep research on machine learning and neural computing, but also helps the general engineering technique people use neural networks to settle the problems in real world. The research on achieve method of neural network ensemble is mainly focus on two aspects , they are how to combine the output results of the several neural networks and how to create the individual neural network of the neural network ensemble.Researches show that the diversity between individual neural networks in neural network ensemble is the main reason of ensemble efficiency. The ensemble is effective only if the neural networks forming it are accurate and make different errors, that is the errors made by them distribute in different data space and the error made by one neural network can compensate by other neural networks in ensemble. From the degree of ensemble diversity, the individual neural network in ensemble can be produced through two methods: direct produce method, over produce and select method. Over produce and select method first produces a large set of neural networks, then based on some rules, selects neural networks to form neural network ensemble. This method makes the diversity between the neural networks in ensemble.In this paper neural networks are used for classifying problems and we do some researches on how to produce individual neural network in ensemble and how to combine their output results.Firstly, based on fuzzy C-means clustering, a method for neural network ensemble is proposed. Using membership function, a distributed function is constructed and based on it, data are sampled from training samples. Then these data are used as training set of individual neural networks, many individual neural networks constitute neural network ensemble and the output of the ensemble uses majority-voting method. Theoretical analysis and experimental results show that this neural network ensemble method can improve the performance of pattern recognition.In addition, based on fuzzy C-means clustering, another method for neural network ensemble is proposed. This method computes the distance between each test sample and the cluster center and transforms the distance to the membership, weighted ensemble neural networks are performed on every result of the individual neural network. The output of the ensemble uses weighted average method. Theoretical analysis and experimental results show that this neural network ensemble method is efficient for pattern classification.Secondly, neural network ensemble is an important approach developing high performance pattern classification system. Previous work shows that the ensemble is effective only if the neural networks forming it are accurate and make different errors. A method of neural network ensemble by clustering and selection is proposed. The feature space is partitioned into disjoined regions, which give the dismission scores of neural networks in the ensemble. Total score decided by all regions orders the preferential rank for one neural network dismission, by which a set of neural networks is selected from original neural network ensemble. Theoretic analysis and experiment results show that the neural network ensemble method based on clustering is efficient for pattern recognition.In the third section, based on the diversity between neural networks, a method for neural network ensemble is proposed. Using diversity measure, this method selects some individual neural networks which satisfies specify conditions, and then these individual neural networks constitute neural network ensemble. The selected individual networks satisfy both individual accuracy and diversity. Theoretical analysis and experimental results show that using this method can improve the accuracy of pattern classification.