SE-BLS: A Shapley-Value-Based Ensemble Broad Learning System with collaboration-based feature selection and CAM visualization

Miao, Jianguo; Liu, Xuanxuan; Guo, Li; Chen, Long

doi:10.1016/j.knosys.2024.112343

Knowledge-Based Systems

2024

DOI: 10.1016/j.knosys.2024.112343

|View full text |Cite

SE-BLS: A Shapley-Value-Based Ensemble Broad Learning System with collaboration-based feature selection and CAM visualization

Jianguo Miao,

Xuanxuan Liu,

Li Guo

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Practical Study of Biological Big Data Base Assisting the Reformed Teaching of Probability Theory and Mathematical Statistics Course

Zeng

2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

Under the background of the large-scale popularization of “Internet + Education”, personalized education mode based on artificial intelligence and other information technologies has become the trend of teaching reform development. In this paper, we have collected and pre-processed students’ learning behaviors during the teaching of Probability Theory and Mathematical Statistics courses, and established a large biological database based on these behaviors. The features of learning behaviors are extracted using the SHAP method, and the features are fused and processed by the MFCNN module. The Bi-LSTM method is used to warn students of academic risks. Finally, we construct a visualization system to assist in teaching Probability Theory and Mathematical Statistics. The results of practical application show that with the assistance of the system, the average grade of the students’ Probability Theory and Mathematical Statistics course progresses quite obviously, the post-test grade is significantly higher than the pre-test grade, and the student’s ability of critical reflection also improves. In addition, a significant difference (p<0.05) was observed between the students of the experimental class and those of the control class in the learning motivation posttest. This paper provides new ideas for research related to the big database of biology and academic early warning in teaching, which contributes to the further promotion of teaching reform.

show abstract

A Practical Study of Biological Big Data Base Assisting the Reformed Teaching of Probability Theory and Mathematical Statistics Course

Zeng

2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

SE-BLS: A Shapley-Value-Based Ensemble Broad Learning System with collaboration-based feature selection and CAM visualization

Cited by 1 publication

References 68 publications

A Practical Study of Biological Big Data Base Assisting the Reformed Teaching of Probability Theory and Mathematical Statistics Course

A Practical Study of Biological Big Data Base Assisting the Reformed Teaching of Probability Theory and Mathematical Statistics Course

Contact Info

Product

Resources

About