Interpretable XGBoost-SHAP machine learning model for identifying scientific breakthroughs
Crossref DOI link: https://doi.org/10.1007/s11192-025-05497-7
Published Online: 2025-12-01
Published Print: 2025-12
Update policy: https://doi.org/10.1007/springer_crossmark_policy
Sheng, Libo
Ruan, Xuanmin
Wang, Yi
Lyu, Dongqing
Cheng, Ying https://orcid.org/0000-0002-0664-7206
Funding for this research was provided by:
National Natural Science Foundation of China (72304133)
Text and Data Mining valid from 2025-12-01
Version of Record valid from 2025-12-01
Article History
Received: 20 June 2024
Accepted: 17 November 2025
First Online: 1 December 2025