Top Research Papers on XGBoost

The proposed Xgboost model outperformed both the Artificial Neural Network and Support Vector Regression models for all different input combinations and serves as a great benchmark for future groundwater levels prediction using Xg Boost algorithm.

This study presented a novel data-driven approach to condition monitoring systems by utilizing the existing Supervisory Control And Data Acquisition (SCADA) system and integrating a wide range of machine learning and data mining techniques to design a Normal Behaviour Model of the generator for fault detection purposes.

Analysis of computational cost efficiency and AUC analysis showed that the Hyperband approach was much faster than the GA in hyperparameter tuning, and thus appeared to be the best optimization algorithm for the problem under consideration.

This work intended to propose natural gradient boosting (NGBoost), a novel member of the ensemble learning family, for modeling landslide susceptibility for Macka County of Trabzon province, Turkey, and indicated that the NGBoost method utilized for landslide susceptibility mapping problem for the first time had the greatest predictive ability.

It is found that the XGBoost model performs better than the ARIMA model in predicting the annual rice production in Bangladesh, and based on the better performance, the study forecasted the annual Rice Production in Bangladesh for the next 10 years using the XTBOost model.

This paper presents one of the most prominent supervised and semi-supervised learning (SSL) machine learning algorithms in a Python environment, XGBoost, which is a parallel tree boost that addresses a variety of data science problems quickly and accurately.

Metering data from the advanced metering infrastructure can be used to find abnormal electricity behavior for the detection of electricity theft, which causes huge financial losses to electric companies every year. This article proposes an electricity theft detector using metering data based on extreme gradient boosting (XGBoost). The metering data are preprocessed, including recover missing and erroneous values and normalization. The classification model based on XGBoost is trained using both benign and malicious samples. Simulations are done by using the Irish Smart Energy Trails data set wi...

An effective adaptive customization platform that encodes the customization data history of a small manufacturing plant, from a static database, into a dynamic machine learning model to produce personalized products for their customers accurately is developed.

A comparative evaluation of soybean yield prediction within the United States shows promising prediction accuracies compared to state-of-the-art yield prediction systems based on Deep Learning.

The XGBoost model introduced in this study is a promising model that can be applied as an efficient estimator for hydrogen solubility in various hydrocarbons and is capable of being utilized in the chemical and petroleum industries.

The boosting model is a kind of ensemble learning technology, including XGBoost and GBDT, which take decision trees as weak classifiers and achieve better results in classification and regression problems. The neural network has an excellent performance on image and voice recognition, but its weak interpretability limits on developing a fusion model. By referring to principles and methods of traditional boosting models, we proposed a Neural Network Boosting (NNBoost) regression, which takes shallow neural networks with simple structures as weak classifiers. The NNBoost is a new ensemble learni...

Comparative experiments demonstrated that the XGBoost method has a remarkable performance in predicting the stage of cancer patients with multi-omics data and identification of novel candidate genes associated with cancer stages would contribute to further elucidate disease pathogenesis and develop novel therapeutics.

This study presents machine learning-based algorithms, including extreme gradient boosting (XGBOOST), deep neural network (DNN), and random forest (RF), to predict green building costs, designed to consider the influence of soft and hard cost-related attributes.

The study suggests that all developed tree-based ensemble models could reliably estimate soil liquefaction and the XGBoost with the Boruta model achieved the most stable and better prediction performance than the other models in all considered cases.

It can be inferred that XGBoost is applicable for streamflow forecasting, and in general, performs better than SVM; the cluster analysis-based modular model is helpful in improving accuracy and capturing the complicated patterns of hydrological process.

This study produces landslide susceptibility map of the Ayancik district of Sinop province, situated in the Black Sea region of Turkey using three featured regression tree-based ensemble methods including gradient boosting machines (GBM), extreme gradient boosting (XGBoost), and random forest (RF).

Reliability analysis approach provides a rational means to quantitatively evaluate the safety of geotechnical structures from a probabilistic perspective. However, it suffers from a known criticism of extensive computational requirements and poor efficiency, which hinders its application in the reliability analysis of earth dam slope stability. Until now, the effects of spatially variable soil properties on the earth dam slope reliability remain unclear. This calls for a novel method to perform reliability analysis of earth dam slope stability accounting for the spatial variability of soil pro...

This study presents a new approach for predicting the load-carrying capacity of reinforced concrete (RC) columns reinforced with fiber-reinforced polymer (FRP) bars with an eXtreme Gradient Boosting (XGBoost) algorithm. The proposed XGBoost model was developed based on a comprehensive database containing experimental data for 283 FRP-RC columns collected from the literature. The SHapley Additive exPlanations (SHAP) framework was used to interpret the output of the model. Furthermore, the efficiency and accuracy of the XGBoost model were evaluated and compared with design codes and equations in...

It is demonstrated that the UCS of concrete can be accurately predicted from its compositions and age using the extreme gradient boosting regression (XGB) method, which is more robust, faster to train and more accurate than the ANN and SVM methods as well as other existent ML methods presented in literature.

The results indicated that, with three of the sampling strategies, XGBoost achieved similar and robust simulation results, and SHAP values were useful for analysing the complex relationship between the different drivers of grassland degradation.