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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Vydáno v:	Computational materials science Ročník 248; s. 113601
Hlavní autoři:	Cheng, Duan-jie, Liang, Yong-chao, Pu, Yuan-wei, Chen, Qian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.02.2025
Témata:	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
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