Ensemble learning predicts glass-forming ability under imbalanced datasets

The flowchart of this paper is depicted in the figure. In the data preprocessing stage, two data enhancement strategies, WERCS and SMOGN, were employed and the strategies were evaluated by PCD. For model construction, 12 commonly used ML models were collected and screened by the R2 and CI. Further,...

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Bibliographic Details

Published in:	Computational materials science Vol. 248; p. 113601
Main Authors:	Cheng, Duan-jie, Liang, Yong-chao, Pu, Yuan-wei, Chen, Qian
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.02.2025
Subjects:	Bayesian optimization algorithm Data Enhancement Design of bulk metallic glasses Ensemble learning Glass-forming ability Data Enhancement Design of bulk metallic glasses Glass-forming ability Ensemble learning Bayesian optimization algorithm
ISSN:	0927-0256
Online Access:	Get full text
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