Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories

2019 ◽  
Vol 118 ◽  
pp. 505-518 ◽  
Author(s):  
Jian Zhou ◽  
Enming Li ◽  
Shan Yang ◽  
Mingzheng Wang ◽  
Xiuzhi Shi ◽  
...  
Keyword(s):  
Slope Stability ◽  
Gradient Boosting ◽  
Stability Prediction ◽  
Case Histories ◽  
Gradient Boosting Machine ◽  
Mode Failure

scholarly journals A Multi-Class Automatic Sleep Staging Method Based on Photoplethysmography Signals

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 116
Author(s):  
Xiangfa Zhao ◽  
Guobing Sun
Keyword(s):  
Time Domain ◽  
Single Channel ◽  
Kappa Statistic ◽  
Gradient Boosting ◽  
Sleep Staging ◽  
Challenging Problem ◽  
Sleep State ◽  
Light Gradient ◽  
Gradient Boosting Machine ◽  
The Time Domain

Automatic sleep staging with only one channel is a challenging problem in sleep-related research. In this paper, a simple and efficient method named PPG-based multi-class automatic sleep staging (PMSS) is proposed using only a photoplethysmography (PPG) signal. Single-channel PPG data were obtained from four categories of subjects in the CAP sleep database. After the preprocessing of PPG data, feature extraction was performed from the time domain, frequency domain, and nonlinear domain, and a total of 21 features were extracted. Finally, the Light Gradient Boosting Machine (LightGBM) classifier was used for multi-class sleep staging. The accuracy of the multi-class automatic sleep staging was over 70%, and the Cohen’s kappa statistic k was over 0.6. This also showed that the PMSS method can also be applied to stage the sleep state for patients with sleep disorders.

scholarly journals Assessing fracture risk using gradient boosting machine (GBM) models

2012 ◽  
Vol 27 (6) ◽  
pp. 1397-1404 ◽  
Author(s):  
Elizabeth J Atkinson ◽  
Terry M Therneau ◽  
L Joseph Melton ◽  
Jon J Camp ◽  
Sara J Achenbach ◽  
...  
Keyword(s):  
Fracture Risk ◽  
Gradient Boosting ◽  
Gradient Boosting Machine

scholarly journals Temporal mixture ensemble models for probabilistic forecasting of intraday cryptocurrency volume

2021 ◽  
Author(s):  
Nino Antulov-Fantulin ◽  
Tian Guo ◽  
Fabrizio Lillo
Keyword(s):  
Econometric Models ◽  
Gradient Boosting ◽  
Point Estimate ◽  
Order Book ◽  
Probabilistic Forecasting ◽  
Short Term ◽  
Ensemble Models ◽  
Gradient Boosting Machine ◽  
Different Sources ◽  
Better Than

AbstractWe study the problem of the intraday short-term volume forecasting in cryptocurrency multi-markets. The predictions are built by using transaction and order book data from different markets where the exchange takes place. Methodologically, we propose a temporal mixture ensemble, capable of adaptively exploiting, for the forecasting, different sources of data and providing a volume point estimate, as well as its uncertainty. We provide evidence of the clear outperformance of our model with respect to econometric models. Moreover our model performs slightly better than Gradient Boosting Machine while having a much clearer interpretability of the results. Finally, we show that the above results are robust also when restricting the prediction analysis to each volume quartile.

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