集成重复训练极限学习机的数据分类

极限学习机是一种随机化算法，它随机生成单隐含层神经网络输入层连接权和隐含层偏置，用分析的方法确定输出层连接权。给定网络结构，用极限学习机重复训练网络，会得到不同的学习模型。本文提出了一种集成模型对数据进行分类的方法。首先用极限学习机算法重复训练若干个单隐含层前馈神经网络，然后用多数投票法集成训练好的神经网络，最后用集成模型对数据进行分类，并在10个数据集上和极限学习机及集成极限学习机进行了实验比较。实验结果表明，本文提出的方法优于极限学习机和集成极限学习机。

Ensemble of Retrained Extreme Learning Machine for Data Classification

Extreme learning machine (ELM) is a randomized algorithm which randomly generates the input weights and hidden nodes biases of single-hidden layer feed-forward neural networks (SLFNNs), and then determines the output weights analytically. Given the architecture of SLFNN, we can obtain different learning models by repeatedly training SLFNNs with ELM. The paper proposes an approach by integrating these learning models for data classification. Specifically, firstly several SLFNNs are trained by ELM. Secondly the trained SLFNNs are integrated by majority voting method. Finally the integrated model is used for data classification. We experimentally compared the proposed approach with ELM and ensemble ELM (EELM) on 10 data sets. Experimental results show that the proposed approach outperforms ELM and EELM.