一种基于互信息变量选择的极端学习机算法

针对回归问题中存在的变量选择和网络结构设计问题, 提出一种基于互信息的极端学习机(ELM) 训练算法, 同时实现输入变量的选择和隐含层的结构优化. 该算法将互信息输入变量选择嵌入到ELM网络的学习过程之中, 以网络的学习性能作为衡量输入变量与输出变量相关与否的指标, 并以增量式的方法确定隐含层节点的规模.在Lorenz、Gas Furnace 和10 组标杆数据上的仿真结果表明了所提出算法的有效性. 该算法不仅可以简化网络结构, 还可以提高网络的泛化性能.

An extreme learning machine algorithm based on mutual information variable selection

To solve the problems of variable selection and architecture design in regression, an extreme learning machine(ELM) based on mutual information is proposed, which can optimize the input layer and the hidden layer simultaneously. The mutual information variable selection is combined with ELM. The performance of the network is used as the criterion of variable selection, and the size of the hidden layer is determined by using the incremental method. Simulation results on two data sets of multivariate time series and 10 benchmark datasets show the effectiveness of the proposed algorithm. The proposed algorithm can not only compact the architecture of the network, but also improve the generalization performance.