Robust machine learning and ensemble learning approach to predict variation in experimental data for multiple measurements and anomalies
Crossref DOI link: https://doi.org/10.1007/s44211-026-00919-9
Published Online: 2026-04-24
Published Print: 2026-07
Update policy: https://doi.org/10.1007/springer_crossmark_policy
Sakai, Yuta
Katayama, Motosuke
Kaneko, Hiromasa https://orcid.org/0000-0001-8367-6476
Funding for this research was provided by:
Meiji University
Text and Data Mining valid from 2026-04-24
Version of Record valid from 2026-04-24
Article History
Received: 8 January 2026
Accepted: 15 April 2026
First Online: 24 April 2026
Declarations
:
: The authors declare no competing interests.