Strength of Stacking Technique of Ensemble Learning in Rockburst Prediction with Imbalanced Data: Comparison of Eight Single and Ensemble Models

doi:10.1007/s11053-020-09787-0

	Strength of Stacking Technique of Ensemble Learning in Rockburst Prediction with Imbalanced Data: Comparison of Eight Single and Ensemble Models
	Yin, Xin 1,2; Liu, Quansheng 1,2; Pan, Yucong 1,2; Huang, Xing 3; Wu, Jian 1,2; Wang, Xinyu 1,2
刊名	NATURAL RESOURCES RESEARCH
	2021-01-03
页码	21
关键词	Rockburst prediction Ensemble learning Stacking technique Class imbalance Outlier detection and substitution
ISSN号	1520-7439
DOI	10.1007/s11053-020-09787-0
英文摘要	Rockburst is a common dynamic geological hazard, severely restricting the development and utilization of underground space and resources. As the depth of excavation and mining increases, rockburst tends to occur frequently. Hence, it is necessary to carry out a study on rockburst prediction. Due to the nonlinear relationship between rockburst and its influencing factors, artificial intelligence was introduced. However, the collected data were typically imbalanced. Single algorithms trained by such data have low recognition for minority classes. In order to handle the problem, this paper employed stacking technique of ensemble learning to establish rockburst prediction models. In total, 246 sets of data were collected. In the preprocessing stage, three data mining techniques including principal component analysis, local outlier factor and expectation maximization algorithm were used for dimension reduction, outlier detection and outlier substitution, respectively. Then, the pre-processed data were split into a training set (75%) and a test set (25%) with stratified sampling. Based on the four classical single intelligent algorithms, namely k-nearest neighbors (KNN), support vector machine (SVM), deep neural network (DNN) and recurrent neural network (RNN), four ensemble models (KNN-RNN, SVM-RNN, DNN-RNN and KNN-SVM-DNN-RNN) were built by stacking technique of ensemble learning. The prediction performance of eight models was evaluated, and the differences between single models and ensemble models were analyzed. Additionally, a sensitivity analysis was conducted, revealing the importance of input variables on the models. Finally, the impact of class imbalance on the prediction accuracy and fitting effect of models was quantitatively discussed. The results showed that stacking technique of ensemble learning provides a new and promising way for rockburst prediction, which exhibits unique advantages especially when using imbalanced data.
资助项目	National Natural Science Foundation of China[41941018] ; National Natural Science Foundation of China[41807250] ; China Postdoctoral Science Foundation Program[2019T120686] ; National Key Basic Research Program of China[2015CB058102]
WOS研究方向	Geology
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000604467700002
内容类型	期刊论文
源URL	[http://119.78.100.198/handle/2S6PX9GI/25486]
专题	中科院武汉岩土力学所
通讯作者	Liu, Quansheng; Pan, Yucong
作者单位	1.Wuhan Univ, Sch Civil Engn, Key Lab Geotech & Struct Engn Safety Hubei Prov, Wuhan 430072, Peoples R China 2.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China 3.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China
推荐引用方式 GB/T 7714	Yin, Xin,Liu, Quansheng,Pan, Yucong,et al. Strength of Stacking Technique of Ensemble Learning in Rockburst Prediction with Imbalanced Data: Comparison of Eight Single and Ensemble Models[J]. NATURAL RESOURCES RESEARCH,2021:21.
APA	Yin, Xin,Liu, Quansheng,Pan, Yucong,Huang, Xing,Wu, Jian,&Wang, Xinyu.(2021).Strength of Stacking Technique of Ensemble Learning in Rockburst Prediction with Imbalanced Data: Comparison of Eight Single and Ensemble Models.NATURAL RESOURCES RESEARCH,21.
MLA	Yin, Xin,et al."Strength of Stacking Technique of Ensemble Learning in Rockburst Prediction with Imbalanced Data: Comparison of Eight Single and Ensemble Models".NATURAL RESOURCES RESEARCH (2021):21.