Imbalanced rock burst assessment using variational autoencoder-enhanced gradient boosting algorithms and explainability

We conducted a study to evaluate the potential and robustness of gradient boosting algorithms in rock burst assessment, established a variational autoencoder (VAE) to address the imbalance rock burst dataset, and proposed a multilevel explainable artificial intelligence (XAI) tailored for tree-based...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Underground space (Beijing) Jg. 17; S. 226 - 245
Hauptverfasser:	Lin, Shan, Liang, Zenglong, Dong, Miao, Guo, Hongwei, Zheng, Hong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Shanghai Elsevier B.V 01.08.2024 KeAi Publishing Communications Ltd KeAi Communications Co., Ltd
Schlagworte:	Algorithms Artificial intelligence Coal mining Correlation analysis Datasets Deep learning Energy consumption Engineering Ensemble learning Explainable artificial intelligence Explainable artificial intelligence (XAI) Gradient boosting Machine learning Mechanics Methods Mines Neural networks Regression models Resampling Rock burst Rockbursts Scientific visualization Sensitivity analysis Underground construction VAE China VAE Explainable artificial intelligence (XAI) Ensemble learning Rock burst Gradient boosting
ISSN:	2467-9674, 2096-2754, 2467-9674
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!