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...

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Bibliographic Details
Published in:	Underground space (Beijing) Vol. 17; pp. 226 - 245
Main Authors:	Lin, Shan, Liang, Zenglong, Dong, Miao, Guo, Hongwei, Zheng, Hong
Format:	Journal Article
Language:	English
Published:	Shanghai Elsevier B.V 01.08.2024 KeAi Publishing Communications Ltd KeAi Communications Co., Ltd
Subjects:	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 Access:	Get full text
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