SIMULTANEOUS NODE PRUNING OF INPUT AND HIDDEN LAYERS USING GENETIC ALGORITHMS
In optimizing the neural network structure, there are two methods: the pruning scheme and the constructive scheme. This paper uses the pruning scheme to optimize neural network structure. The genetic algorithm is used to find out the optimum node pruning. In the conventional researches, the input and hidden layers were optimized separately. On the contrary we attempted to optimize the two layers simultaneously by encoding two layers in a chromosome. The offspring networks inherit the weights from the parent For learning, we used the existing error back-propagation algorithm. In our experiment with various databases from LCI Machine Learning Repository, we could get the peak performance when the network size was reduced by about 8～25％. As a result of t-test the proposed method was shown to have a better performance, compared with other pruning or construction methods.
Optimization of Neural Networks Genetic Algorithm Node Pruning Cross-Validation
GI-SU HEO IL-SEOK OH
Department of Computer and Information science, Chonbuk National University, Jeonju, Korea
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
3428-3433
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）