Evaluation of Sino Foreign Cooperative Education Project Using Orthogonal Sine Cosine Optimized Kernel Extreme Learning Machine

Abstract

This study aims to propose an efficient evaluation model for Sino foreign cooperative education projects, which can offer a reasonable reference for universities to deepen reform and innovation of education and further enhance the level of international education. The core engine of the model is the kernel extreme learning machine (KELM) model integrated with orthogonal learning (OL) strategy optimization. The introduction of the OL mechanism is to further strengthen the optimization capabilities of the basic SCA, which is devoted to promoting the KELM model to select the optimal parameter combination and feature subset and further enhance the KELM evaluation capability of Sino foreign cooperative education projects. To examine the performance of the proposed method, OLSCA is evaluated on 23 benchmark problems, comparison with eight other well-known methods. The experimental results have shown that the proposed OLSCA is prominently superior to existing methods on most functional problems. Meantime, OLSCA-KELM is compared against other machine learning approaches in dealing with the evaluation of education projects of Sino foreign cooperation. The simulation results illustrate that the presented OLSCA-KELM obtains better performance of classification and higher stability on all four indicators. Therefore, it is evident that the presented OLSCA-KELM can be an effective solution for the evaluation of Sino foreign cooperative education projects. INDEX TERMS Sine cosine algorithm, swarm intelligence, sino foreign cooperative education project, Kernel extreme learning machine, parameter optimization. HUILING CHEN received the Ph.D. degree from the Department of Computer Science and Technology, Jilin University, China. He is currently an Associate Professor with the College of Computer Science and Artificial Intelligence, Wenzhou University, China. He has published more than 100 articles in international journals and conference proceedings, including Information Sciences, Pattern Recognition, Future Generation Computer System, Expert Systems with Applications, Knowledge-Based Systems, Neurocomputing, PAKDD, and among others. His current research interests include machine learning and data mining, as well as their applications to medical diagnosis and bankruptcy prediction. He is also a Reviewer of many journals such as Applied Soft Computing, Artificial Intelligence in Medicine, Knowledge-Based Systems, and Future Generation Computer System. He has been serving as an Associate Editor for IEEE ACCESS.

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