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.

Pyrolysis is a thermochemical pathway widely used for the conversion of biomass into useful products such as biochar, bio-oil, and syngases. A recent surge in the adoption of the pyrolysis process at realtime scenarios for the appropriate management and conversion of residues demands the modeling of the pyrolysis process. Prediction of algal biochar yield along with its composition was attempted in this study with the eXtreme Gradient Boosting (XGB) machine learning method. An extensive grid search method has been implemented in the XGB model to explore all the possible considered input parame...

This paper aims to present the application of extreme gradient boosting (XGBoost) to the prediction of the punching shear resistance of reinforced concrete (R/C) interior slabs without shear reinforcement. For the training and testing of the XGBoost model, which was developed using the XGBoost 1.1.1 package, 497 experimental data of interior slab-column connections were collected from the literature. The input variables were the column section dimension, slab effective depth, concrete compressive strength, steel yield strength, and reinforcement ratio at the top and bottom of the slab. The tar...

Novel data-driven extreme gradient boosting (XGBoost) and random forest ensemble learning methods are applied for capturing the relationships between the USS and various basic soil parameters to predict undrained shear strength of soft clays.

Active travel has environmental, social, and public health-related benefits. Researchers from diverse domains have extensively studied built-environment associations with active travel. However, limited attention has been paid to distinguishing the associations between built environment characteristics at both the origins and destinations and active travel for working and shopping. Scholars have started to examine non-linear associations of built environment with travel behaviour, but active travel has seldom been a focus. Therefore, this study, selecting Xiamen, China, as the case, utilises a...

The proposed ensemble model, which consists of two advance base models, namely extreme gradient boosting forest and deep neural networks (XGBF-DNN), is proposed for hourly global horizontal irradiance forecast and exhibits the best combination of stability and prediction accuracy irrespective of seasonal variations in weather conditions.

A comprehensive comparison between XGBoost, LightGBM, CatBoost, random forests and gradient boosting has been performed and indicates that CatBoost obtains the best results in generalization accuracy and AUC in the studied datasets although the differences are small.

The use of these boosting algorithms especially optimized with PSO is recommended for predicting the occurrence of liquefaction-induced lateral spreading, and PSO-CatBoost outperformed other state-of-the-art models in terms of performance metrics.

A finite-element analysis considering the anisotropy for the undrained shear strength was performed to examine the effects of the total stress-based anisotropic model NGI-ADP (developed by Norwegian Geotechnical Institute based on the Active-Direct simple shear-Passive concept) parameters on the base stability of deep braced excavations in clays. These parameters included the ratio of the plane strain passive shear strength to the plane strain active shear strength suP/suA, the ratio of the unloading/reloading shear modulus to the plane strain active shear strength Gur/suA, the plane strain ac...

This study aims to develop machine learning (ML) models that can predict the seismic responses of planar steel moment-resisting frames subjected to ground motions. For this purpose, two of the most powerful ML techniques, artificial neural network (ANN) and extreme gradient boosting (XGBoost), were applied. To generate a comprehensive dataset for the training and testing of the ML models, 22,464 nonlinear dynamic analyses were conducted on 36 steel moment frames with different structural characteristics (i.e., number of stories, number of bays, column-to-beam moment capacity ratio) subjected t...

Findings reveal that the proposed Jaya-XGBoost emerged as the most reliable model in contrast to other machine learning models and traditional empirical models.

The results showed that the central part of the Bâsca Chiojdului river basin, which covers approximately 30% of the study area, is more susceptible to flash flooding.

NGBoost generalizes gradient boosting to probabilistic regression by treating the parameters of the conditional distribution as targets for a multiparameter boosting algorithm, and shows how the Natural Gradient is required to correct the training dynamics of the authors' multiparameters boosting approach.

This paper studies a practical federated environment with relaxed privacy constraints, where a dishonest party might obtain some information about the other parties' data, but it is still impossible for the dishonest party to derive the actual raw data of other parties.

The incorporation of SWC or/and LAI in the machine learning models is highly recommended for accurate daily maize T estimation, and the DNN model is more effective for daily maizeT estimation due to its advantage in modeling high-order complex relationships between T and its driving variables through multiple levels of feature abstraction.

A novel hybrid model is proposed for predicting short-term building energy consumption using complete ensemble empirical mode decomposition with adaptive noise, and the buildingEnergy consumption is predicted by the traditional extreme gradient boosting.

Groundwater (GW) resources provide a large share of the world's water demand for various sections such as agriculture, industry, and drinking water. Particularly in the arid and semi-arid regions, with surface water scarcity and high evaporation, GW is a valuable commodity. Yet, GW data are often incomplete or nonexistent. Therefore, it is a challenge to achieve a GW potential assessment. In this study, we developed methods to produce reliable GW potential maps (GWPM) with only digital elevation model (DEM)-derived data as inputs. To achieve this objective, a case study area in Iran was select...

A virtual ground-based PM2.5 observation network based on high-density surface meteorological observations using the Extreme Gradient Boosting model shows great potential in reconstructing historical PM 2.5 data, and surface visibility plays the dominant role in terms of the relative importance of variables in the XGBoost model.

The efforts to explore the bacterial diversity associated with desert plants in the arid, semi-arid, and hyper-arids regions are described, highlighting the latest discoveries and applications of plant growth promoting bacteria from the most studied deserts around the world.

Accurate short-term load forecasting of distribution transformer in extreme weather will effectively assist power dispatching and enable safe and stable operation of power grid. Therefore, this paper proposes the Bagging–XGBoost algorithm based extreme weather identification and short-term load forecasting model, which can warn the time period and detailed value of peak load in advance. Firstly, based on Extreme Gradient Boosting (XGBoost) algorithm, the idea of Bagging is introduced to reduce the output variance and enhance the generalization ability of the algorithm. Then, the mutual informa...

This article proposes a general method to learn gradient-boosted decision trees for multiple outputs, called GBDT-MO, which achieves outstanding performance in terms of accuracy, training speed, and inference speed.