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

Author

Listed:
  • 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
    Department of Information Management, Chaoyang University of Technology, Taichung 413310, Taiwan)

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.

Suggested Citation

  • Liya Yue & Pei Hu & Shu-Chuan Chu & Jeng-Shyang Pan, 2023. "Multi-Objective Gray Wolf Optimizer with Cost-Sensitive Feature Selection for Predicting Students’ Academic Performance in College English," Mathematics, MDPI, vol. 11(15), pages 1-16, August.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:15:p:3396-:d:1210069
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    References listed on IDEAS

    as
    1. Arihant Tanwar & Wajdi Alghamdi & Mohammad D. Alahmadi & Harpreet Singh & Prashant Singh Rana, 2023. "A Fuzzy-Based Fast Feature Selection Using Divide and Conquer Technique in Huge Dimension Dataset," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
    2. Sandra González-Gallardo & Ana B. Ruiz & Mariano Luque, 2021. "Analysis of the Well-Being Levels of Students in Spain and Finland through Interval Multiobjective Linear Programming," Mathematics, MDPI, vol. 9(14), pages 1-27, July.
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    4. Qian Zhang & Jinjin Ding & Weixiang Shen & Jinhui Ma & Guoli Li, 2020. "Multiobjective Particle Swarm Optimization for Microgrids Pareto Optimization Dispatch," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, March.
    5. Chuang Liu & Haojie Wang & Zhonghu Yuan, 2022. "A Method for Predicting the Academic Performances of College Students Based on Education System Data," Mathematics, MDPI, vol. 10(20), pages 1-19, October.
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