Multi-Objective Gray Wolf Optimizer with Cost-Sensitive Feature Selection for Predicting Students’ Academic Performance in College English

Liya Yue, Pei Hu, Shu-Chuan Chu and Jeng-Shyang Pan ()
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Liya Yue: Fanli Business School, Nanyang Institute of Technology, Nanyang 473004, China
Pei Hu: School of Computer and Software, Nanyang Institute of Technology, Nanyang 473004, China
Shu-Chuan Chu: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Jeng-Shyang Pan: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

Mathematics, 2023, vol. 11, issue 15, 1-16

Abstract: Feature selection is a widely utilized technique in educational data mining that aims to simplify and reduce the computational burden associated with data analysis. However, previous studies have overlooked the high costs involved in acquiring certain types of educational data. In this study, we investigate the application of a multi-objective gray wolf optimizer (GWO) with cost-sensitive feature selection to predict students’ academic performance in college English, while minimizing both prediction error and feature cost. To improve the performance of the multi-objective binary GWO, a novel position update method and a selection mechanism for a, b, and d are proposed. Additionally, the adaptive mutation of Pareto optimal solutions improves convergence and avoids falling into local traps. The repairing technique of duplicate solutions expands population diversity and reduces feature cost. Experiments using UCI datasets demonstrate that the proposed algorithm outperforms existing state-of-the-art algorithms in hypervolume (HV), inverted generational distance (IGD), and Pareto optimal solutions. Finally, when predicting the academic performance of students in college English, the superiority of the proposed algorithm is again confirmed, as well as its acquisition of key features that impact cost-sensitive feature selection.

Keywords: feature selection; multi-objective gray wolf optimizer; cost-sensitive; academic performance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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