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

Investigating the use of random forest, gradient boosting machine, support vector machine and their ensemble applied to fault detection

2017 ◽  
Author(s):  
Luis Felipe Nogoseke ◽  
Gabriel Herman Bernardim Andrade ◽  
Marco Boaretto ◽  
Leandro Coelho
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Fault Detection ◽  
Gradient Boosting ◽  
Support Vector ◽  
Gradient Boosting Machine

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 Fertility-LightGBM: A fertility-related protein prediction model by multi-information fusion and light gradient boosting machine

2021 ◽  
Vol 68 ◽  
pp. 102630
Author(s):  
Minghui Wang ◽  
Lingling Yue ◽  
Xinhua Yang ◽  
Xiaolin Wang ◽  
Yu Han ◽  
...  
Keyword(s):  
Prediction Model ◽  
Information Fusion ◽  
Gradient Boosting ◽  
Related Protein ◽  
Light Gradient ◽  
Protein Prediction ◽  
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.

scholarly journals Gut microbiome activity contributes to individual variation in glycemic response in adults

2019 ◽  
Author(s):  
Hal Tily ◽  
Ally Perlina ◽  
Eric Patridge ◽  
Stephanie Gline ◽  
Matvey Genkin ◽  
...  
Keyword(s):  
Individual Variation ◽  
Gut Microbiome ◽  
Metabolic Diseases ◽  
Nutritional Composition ◽  
Gradient Boosting ◽  
Glycemic Response ◽  
Activity Data ◽  
Gradient Boosting Machine ◽  
Functional Features ◽  
First Time

AbstractLimiting post-meal glycemic response is an important factor in reducing the risk of chronic metabolic diseases, and contributes to significant health benefits in people with elevated levels of blood sugar. In this study, we collected gut microbiome activity (i.e., metatranscriptomic) data and measured the glycemic responses of 550 adults who consumed more than 30,000 meals from omnivore or vegetarian/gluten-free diets. We demonstrate that gut microbiome activity makes a statistically significant contribution to individual variation in glycemic response, in addition to anthropometric factors and the nutritional composition of foods. We describe predictive models (multilevel mixed-effects regression and gradient boosting machine) of variation in glycemic response among individuals ingesting the same foods. We introduce functional features aggregated from microbial activity data as candidates for association with mechanisms of glycemic control. In summary, we demonstrate for the first time that metatranscriptomic activity of the gut microbiome is correlated with glycemic response among adults.

scholarly journals Experimental Analysis of GBM to Expand the Time Horizon of Irish Electricity Price Forecasts

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7587
Author(s):  
Conor Lynch ◽  
Christian O’Leary ◽  
Preetham Govind Kolar Sundareshan ◽  
Yavuz Akin
Keyword(s):  
Electricity Market ◽  
Electricity Consumption ◽  
Cost Effective ◽  
Absolute Error ◽  
Electricity Price ◽  
Gradient Boosting ◽  
Avant Garde ◽  
Electricity Price Forecasting ◽  
Gradient Boosting Machine ◽  
Extreme Gradient Boosting

In response to the inherent challenges of generating cost-effective electricity consumption schedules for dynamic systems, this paper espouses the use of GBM or Gradient Boosting Machine-based models for electricity price forecasting. These models are applied to data streams from the Irish electricity market and achieve favorable results, relative to the current state-of-the-art. Presently, electricity prices are published 10 h in advance of the trade day of interest. Using the forecasting methodology outlined in this paper, an estimation of these prices can be made available one day in advance of the official price publication, thus extending the time available to plan electricity utilization from the grid to be as cost effectively as possible. Extreme Gradient Boosting Machine (XGBM) models achieved a Mean Absolute Error (MAE) of 9.93 for data from 30 September 2018 to 12 December 2019 which is an 11.4% improvement on the avant-garde. LGBM models achieve a MAE score 9.58 on more recent data: the full year of 2020.

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