DE-ELM-SSC+：Semi-supervised Classification Algorithm

• Main Author: PANG Jun, HUANG Heng, ZHANG Shou, SHU Zhiliang, ZHAO Yuhai
• Format: Article
• Language: zho
• Published: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 2020-12-01
• Series: Jisuanji kexue yu tansuo
• Subjects: extreme learning machine (elm); semi-supervised classification; strategy selection; differential evolution (de); scaling factor
• Online Access: http://fcst.ceaj.org/CN/abstract/abstract2482.shtml

The combinations of evolutionary algorithms (EA) and analytical methods have been extensively studied in the fields of machine learning in recent years. This paper focuses on how to combine a differential evolution (DE) algorithm with the semi-supervised classification algorithm based on extreme learning machine (ELM). Firstly, this paper proposes a semi-supervised classification algorithm based on DE and ELM (DE-ELM-SSC) with roughly three steps. Firstly, multiple differential evolution strategies are adopted to optimize the input weights and hidden biases of extreme learning machine, and an optimal strategy for the target data set is selected according to the root mean square error (RMSE). Secondly, the optimal evolutionary strategy selected in the previous step is applied to the DE algorithm to optimize the ELM network parameters. Thirdly, in order to construct a semi-supervised classification model, tri-training technology is used to realize the cooperative training of three improved ELM base classifiers. Then, a nonlinear method is adopted to improve the existing inertial strategy method and realize adaptive adjustment of scaling factor, so as to optimize the DE-ELM-SSC algorithm to obtain the DE-ELM-SSC+ algorithm. Lastly, a large number of experimental results on UCI data sets show that the DE-ELM-SSC+ algorithm outperforms the baseline methods with higher classification accuracy because of evolution strategy selection and improved scaling factor adaptive adjustment.