关键词: 极限学习机, 差分进化, Tri-Training算法, 半监督学习

Abstract: When Extreme Learning Machine（ELM） is used, unlabeled samples are not fully utilized and the training accuracy is affected by the initial value of the network. Aiming at this problem, this paper proposes a modified ELM algorithm based on cooperative training and differential evolution（Tri-DE-ELM）. Firstly, considering the issue that traditional ELM pattern classification techniques only use a small amount of labeled samples and ignore a large number of unlabeled samples, co-training mechanism based on Tri-training algorithm is applied to construct the semi-supervised Tri-ELM classification algorithm, which trains three base classifiers using small amount of labeled samples to achieve the labels of the unlabeled sample. Further, in view of the problem that the classification results are affected by random initialization of the ELM network input layer weights, Differential Evolution（DE） algorithm is used to optimize the initial value of the network. The optimization procedure considers two factors of network weights and the classification error, to avoid over fitting phenomenon. Experimental results on standard data sets show that the Tri-DE-ELM algorithm can utilize unlabeled data effectively, and has a higher classification accuracy than the traditional ELM.

Key words: Extreme Learning Machine（ELM）, differential evolution, Tri-Training algorithm, semi-supervised learning