• 学术研究 •

### DE-ELM-SSC+半监督分类算法

1. 1. 武汉科技大学 计算机科学与技术学院，武汉 430070
2. 智能信息处理与实时工业系统湖北省重点实验室，武汉 430070
3. 东北大学 计算机科学与工程学院，沈阳 110169
• 出版日期:2020-12-01 发布日期:2020-12-11

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

PANG Jun, HUANG Heng, ZHANG Shou, SHU Zhiliang, ZHAO Yuhai

1. 1. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430070, China
2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan 430070, China
3. School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China
• Online:2020-12-01 Published:2020-12-11

Abstract:

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.