Top Research Papers on XGBoost

The machine learning technique used to predict diabetes in this study is extreme gradient boosting (XGBoost), an advanced implementation of gradient boosting along with multiple regularization factors to accurately predict target variables by combining simpler and weaker model set estimations.

This article presents a study on forecasting silver prices using the extreme gradient boosting (XGBoost) machine learning method with hyperparameter tuning, and finds that the proposed models exhibited the best performance.

Globally, the consistent clamor by environmentalists for the need to mitigate the effects of climate change has necessitated the adoption of renewable energy sources (RES) for use by many developed/developing nations. The approach is geared towards gradually replacing or reducing the use of fossil fuels for electric power production. Solar power is among the major renewable energy sources in use today. But the use of solar energy is, unfortunately, characterized by fluctuations in its power generation due to the unpredictability of solar irradiance. Despite many methods in use already, accurat...

Results show that the XGBOOST model is efficient in generating future stock prices and performs with a mean squared error of 0.004, mean absolute error of 0.014 and R2 score of 0.995.

This study aims to classify air quality into three labels or categories: good, moderate, and unhealthy, using XGBoost, a development of the Gradient Decision Tree with several advantages, such as high scalability and prevention of overfitting.

Abstract Ripe fruit provides essential nutrients for the human body. To fulfill the needs of consumers, the practice of artificial ripening has become more common. Artificial ripening not only degrades the quality of fruit but also impairs health. The potential of hyperspectral imaging coupled with machine learning to quickly and uninvasively identify differently ripened bananas was explored in this study. A total of 300 banana samples that were naturally ripened or ripened by ethephon or calcium carbide were characterized by their hyperspectral images. To improve the accuracy of classificatio...

Vehicle to vulnerable road user (VRU) crashes occupy a large proportion of traffic crashes in China, and crash injury severity analysis can support traffic managers to understand the implicit rules behind the crashes. Therefore, 554 VRUs-involved crashes are collected from January, 2017, to February, 2021, in a city in northern China, including 322 vehicle-pedestrian crashes and 232 vehicle-bicycle crashes. First, a descriptive statistical analysis is conducted to investigate the characteristics of VRUs-involved crashes. Second, the extreme gradient boosting (XGBoost) model is introduced to id...

This study focuses on the development of an electrical demand forecasting model using machine learning techniques, specifically Long Short-Term Memory (LSTM) and eXtreme Gradient Boosting (XGBoost), and uses data collected from wireless sensors installed in the city.

The result of this study shows that the machine learning model created using XGBoost has an accuracy value of 89%, with the precision value of 90% and recall value 80%.

It is proved that the use of algorithms with hyperparameter tuning can improve the performance of eXtreme Gradient Boosting algorithm in the process of classification of credit card customers with an accuracy of 80.039%, precision of 81.338% and a recall value of 96.854%.

An Evolutionary Algorithm named as Group Counseling Optimizer (GCO) in tandem with Extreme gradient boost (XGBoost) to classify cancer in microarray data is employed.

This paper proposes to use the XGBoost model for annual crop yield prediction in Malaysia and shows promising results with 0.98 R-Squared value and outperformed the current models.

This research introduces a new approach for predicting short-term DC power loads using the XgBoost algorithm that utilizes weak learners to build robust prediction models using weaker learning combinations and shows the ability to capture complex non-linear patterns based on real-life residential DC load data.

XGBoost algorithm gives a better prediction than LogReg algorithm does based on its prediction accuracy, specificity, sensitivity and ROC curve and has a better capability to separate churned customers from not-churned customers than LogReg model does according to KS chart and Gains-Lift charts produced by each algorithm.

The results show that the XGBoost model outperforms the IDM in terms of prediction errors and the analysis of the feature importance reveals that the longitudinal position has the greatest influence on vehicle trajectory prediction results.

This study aims to fill the research gap by creating an ECG classification model to detect arrhythmia using the XGBoost algorithm, and the results are quite good for each class, but are still considered not better than those models.

By demonstrating how PCA can improve the efficiency and accuracy of prediabetes prediction models, this research provides valuable insights to inform future studies and contribute to the development of more effective diagnostic tools for early detection and prevention of prediabetes.

Extreme gradient boosting is the machine learning technique to predict diabetes (xgboost) using Feature Importance XGBoost using UCI Machine Learning to produce a model that can be used by medical staff to predict and identify diabetes in patients.

This specific task focuses on anomaly-based society intrusion detection by employing XGBoost algorithm on KDD CUP 1999 info positioned to get the ideal outcomes.

It can be concluded that the implementation of XGBoost Feature Importance can be used to improve LightGBM's performance in bankruptcy prediction, and this study aims to implement a machine learning model using the Light Gradientboosting Machine (LightGBM) classification algorithm which is optimized using Extreme Gradient Boosting (XGBeost) Feature Importation to increase the accuracy of bankruptcy prediction.

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.

A data-driven FAS framework as a prototype that stakeholders can utilize solely based on their gauging information for local flood warning and mitigation practices is illustrated.

This research will focus on making forecasting applications and analyzing the exchange rate of USD against rupiah based on time series data or temporal datasets from the Investing.com site using machine learning methods, namely Extreme Gradient Boosting (XGBoost).

This paper will be focusing on the implementation of Genetic Algorithm as feature selection when applied to the widely used machine learning algorithm Random Forest and Extreme Gradient Boosting for cancer classification and the result will show that with 20% testing data, XGBoost with Genetic Al algorithm asfeature selection gives the highest accuracy score.

This work considers a supervised approach, namely eXtreme Gradient Boosting (XGBoost) algorithm, which has never been investigated for TC, and obtains 99.5% accuracy on a dataset containing real flows.

The utility of XGBoost in detecting examinees with potential item preknowledge is investigated using a real data set that includes examinees who engaged in fraudulent testing behavior, such as illegally obtaining live test content before the exam.

The results indicate that the XGBoost-PCA model performed better than the other models for predicting common odor descriptors, and may be helpful in understanding the structure-odor relationship.

SubMito-XGBoost has obtained satisfactory prediction results by the leave-one-out-cross-validation (LOOCV) compared with existing methods and achieves satisfactory predictive performance on plant and non-plant protein submitochondrial datasets.

Kanker serviks yang juga biasa dikenal dengan kanker mulut leher rahim merupakan satu dari beberapa jenis penyakit kanker yang mematikan pada wanita setelah kanker payudara. Menurut survei WHO, dari total kasus kanker di Indonesia, 9,2% kasus di antaranya adalah kanker serviks dengan jumlah 36.633 kasus. Sulitnya menentukan gejala awal pada kanker serviks dikarenakan gejala yang timbul tidak kasat mata sehingga banyak sekali kasus terlambat penanganan pada pasien penderita penyakit ini. Penelitian dilakukan dengan metode XGBoost untuk mengklasifikasi gejala awal penyakit kanker serviks dengan ...

This study aims to classify the information data on scientific conferences in Indonesia with a total of 1005 data into three labels, namely Economics, Science and Engineering, and Social Studies using the Extreme Gradient Boost (XGBoost) method.

The characteristics of housing and location conditions are the main drivers of spatial differences in housing prices, which is a topic attracting high interest in both real estate and geography research. One of the most popular models, the hedonic price model (HPM), has limitations in identifying nonlinear relationships and distinguishing the importance of influential factors. Therefore, extreme gradient boosting (XGBoost), a popular machine learning technology, and the HPM were combined to analyse the comprehensive effects of influential factors on housing prices. XGBoost was employed to iden...

To accurately estimate HCV using sparse weather information, this work offers two machine learning methods: The Support Vector Machine (SVM) and a simple tree-based ensemble approach called Extreme Gradient Boosting (XGBoost) that are applied to real-world data on HCV.

The results demonstrate that XGBoost outperforms other models in analyzing sensor data and identifying motion errors and improper robot performance, making it an effective tool in developing AI-driven robots.

The findings suggest that the BO-XGBoost model shows notable classification performance in evaluating the suitability of lunar landing sites, which may provide valuable support for future landing missions and contribute to optimizing lunar exploration efforts.

XGBoost with RFE outperforms other popular machine learning algorithms for NIDS on this dataset, achieving the highest MCC for detecting NSL-KDD dataset attacks of type DoS, Probe, U2R, and R2L and very high classification time.

Mapping the distribution of bamboo species is vital for the sustainable management of bamboo and for assessing its ecological and socioeconomic value. However, the spectral similarity between bamboo species makes this work extremely challenging through remote sensing technology. Existing related studies rarely integrate multiple feature variables and consider how to quantify the main factors affecting classification. Therefore, feature variables, such as spectra, topography, texture, and vegetation indices, were used to construct the XGBoost model to identify bamboo species using the Zhuhai-1 ...

A proactive interpretable prediction model using ML and explainable artificial intelligence (XAI) to detect different types of security attacks using the log data generated by heating, ventilation, and air conditioning attacks is proposed.

The eXtreme Gradient Boosting (XGBoost) algorithm is used for learning and detection, in tandem with Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) for text vectorization.

The model shows notable performance in accuracy and provided an understanding of the estimation results, which show that the total travel time of the local train feature is found to substantially affect the probability of express train preference with a 1.871 SHAP value.

This research takes Twitter data to see public opinion on presidential candidates, and determines the process of digital text analysis and the application of the XGBOOST method to Twitter user sentiment in two categories (positive and negative) and three categories (positive, negative and neutral) for each candidate.

In this paper, Self-Configuration and Self-healing Framework using an extreme gradient boosting (XGBoost) Classifier are proposed and the results exhibit that the proposed framework has higher packet delivery ratio with reduced packet drops and computational cost.

This study designs a system that is capable of classifying SMK student graduates as an evaluation for the school institution to enable the school to devise strategies for producing better student quality in the following year.

This work proposes a malware classification scheme that constructs a model using low-end computing resources and a very large balanced dataset for malware and gives improved performance for accuracy with the tuning of the hyperparameter and achieve higher accuracy.

The main cause of cancer death is lung cancer (LC) which usually presents at an advanced stage, but its early detection would increase the benefits of treatment. Blood is particularly favored in clinical research given the possibility of using it for relatively noninvasive analyses. Copy number variation (CNV) is a common genetic change in tumor genomes, and many studies have indicated that CNV‐derived cell‐free DNA (cfDNA) from plasma could be feasible as a biomarker for cancer diagnosis.

Denial of Service (DoS) adalah salah satu serangan cyber populer yang ditargetkan pada situs web organisasi terkenal dan berpotensi memiliki biaya ekonomi dan waktu yang tinggi. Dalam makalah ini, beberapa metode pembelajaran mesin termasuk model ensemble dan pengklasifikasi deep learning berbasis autoencoder dibandingkan dan disetel menggunakan optimasi Bayesian. Kerangka autoencoder memungkinkan untuk mengekstrak fitur baru dengan memetakan input asli ke ruang baru. Metode tersebut dilatih dan diuji baik untuk klasifikasi biner dan multi-kelas pada kumpulan data Digiturk dan Labris, yang bar...

Rainfall greatly affects human life in various sectors including agriculture, transportation, etc. and also can affect natural disasters such as drought, floods, and landslides. This situation prompts us to build an accurate rainfall prediction model so that prescriptive measures can be made. Previous research on rainfall prediction uses models that have their limitations and thus produce poor performance. This study aims to build a multivariate rainfall prediction model using the best performing technique to date namely the Extreme Gradient Boosting. This model is built based on 7 years of hi...

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.

Extreme gradient boosting (XGBoost) that provides highly efficient and accurate data predictive model were used and may be used for any future network intrusion data where these 23 features are available to easily and efficiently predict network attack types.

This research is to estimate the gourami supplies using transaction data to forecast using the gradient boosting decision tree method from XGBoost, and indicates that the mixed feature model produces an accuracy of 97.54%.

As the availability, size and complexity of data have increased in recent years, machine learning (ML) techniques have become popular for modeling. Predictions resulting from applying ML models are often used for inference, decision-making, and downstream applications. A crucial yet often overlooked aspect of ML is uncertainty quantification, which can significantly impact how predictions from models are used and interpreted. Extreme Gradient Boosting (XGBoost) is one of the most popular ML methods given its simple implementation, fast computation, and sequential learning, which make its predi...