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Journal articles on the topic 'EXtreme gradient boosting (XGB) classifier'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Mukhanova, Ayagoz, Madiyar Baitemirov, Azamat Amirov, et al. "Forecasting creditworthiness in credit scoring using machine learning methods." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 5 (2024): 5534. http://dx.doi.org/10.11591/ijece.v14i5.pp5534-5542.

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This article provides an overview of modern machine learning methods in the context of their active use in credit scoring, with particular attention to the following algorithms: light gradient boosting machine (LGBM) classifier, logistic regression (LR), linear discriminant analysis (LDA), decision tree (DT) classifier, gradient boosting classifier and extreme gradient boosting (XGB) classifier. Each of the methods mentioned is subject to careful analysis to evaluate their applicability and effectiveness in predicting credit risk. The article examines the advantages and limitations of each met

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Mousa, Saleh R., Peter R. Bakhit, and Sherif Ishak. "An extreme gradient boosting method for identifying the factors contributing to crash/near-crash events: a naturalistic driving study." Canadian Journal of Civil Engineering 46, no. 8 (2019): 712–21. http://dx.doi.org/10.1139/cjce-2018-0117.

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Despite the research efforts for reducing traffic accidents, the number of global annual vehicle accidents is still on the rise. This continues to motivate researchers to examine the factors contributing to crash and near-crash events (CNC). Recently, many studies attempted to identify the associated crash factors using naturalistic driving study (SHRP2-NDS) data. Despite the many classifiers developed in the literature, the high dimensionality and multicollinearity within the SHRP2-NDS data limit the accuracy and reliability of the developed models. This study develops an extreme gradient boo

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Chen, Pengzhen. "Research on Mushroom Classification Based on XGB Technology." Advances in Engineering Technology Research 13, no. 1 (2025): 1494. https://doi.org/10.56028/aetr.13.1.1494.2025.

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The mushroom classification problem, as a typical binary classification problem, has become a widely studied object in the field of machine learning. Traditional mushroom classification methods typically rely on manual feature extraction and rule-based criteria establishment, which are often susceptible to human factors, resulting in relatively low classification accuracy. With the development of machine learning technology, especially the emergence of ensemble learning methods, based on various machine learning models, particularly tree-based models, it is possible to efficiently distinguish

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Iqbal, Saqib, Azhar Imran, and Muhammad Adnan. "Breast Tumor Detection using Machine Learning Boosting Classifiers." Journal of Computing & Biomedical Informatics 4, no. 01 (2022): 118–31. http://dx.doi.org/10.56979/401/2022/64.

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Breast cancer is the frequently found in women and the second greatest reason of death worldwide. As breast cancer is detected early, the ratio of survival rate increases because better therapy may be provided. ML algorithms are very vital in the early diagnosis of breast cancer. In this study, we purposed a Novel method that increases the accuracy and performance using these three different classifiers: Gradient Boost (GB), Ada Boost (ABC), and Extreme Gradient Boost (XGB). On the Public dataset WBC, we evaluated and compared the classifiers’ performance and accuracy. Because the chance of ex

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Shao, Chen, and Yue zhong yi Sun. "Shilling attack detection for collaborative recommender systems: a gradient boosting method." Mathematical Biosciences and Engineering 19, no. 7 (2022): 7248–71. http://dx.doi.org/10.3934/mbe.2022342.

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<abstract> <p>Organized malicious shilling attackers influence the output of the collaborative filtering recommendation systems by inserting fake users into the rating matrix within the database. The existence of shilling attack poses a serious risk to the stability of the system. To counter this specific security threat, many attack detection methods are proposed. Some of the past methods suffer from two disadvantages, the first being that they only analyze the rating matrix from a single perspective of user rating values and ignore other perspectives. Another is that some methods

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Afolabi, Hassan A., and Abdurazzag A. Aburas. "Statistical performance assessment of supervised machine learning algorithms for intrusion detection system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 266–77. https://doi.org/10.11591/ijai.v13.i1.pp266-277.

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Several studies have shown that an ensemble classifier's effectiveness is directly correlated with the diversity of its members. However, the algorithms used to build the base learners are one of the issues encountered when using a stacking ensemble. Given the number of options, choosing the best ones might be challenging. In this study, we selected some of the most extensively applied supervised machine learning algorithms and performed a performance evaluation in terms of well-known metrics and validation methods using two internet of things (IoT) intrusion detection datasets, namely network

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Kar, Subhajit, Rajorshi Bhattacharya, Ramkrishna Das, Ylva Pihlström, and Megan O. Lewis. "Classification of Wolf–Rayet Stars Using Ensemble-based Machine Learning Algorithms." Astrophysical Journal 977, no. 2 (2024): 170. https://doi.org/10.3847/1538-4357/ad8dda.

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Abstract We develop a robust machine learning classifier model utilizing the eXtreme-Gradient Boosting (XGB) algorithm for improved classification of Galactic Wolf–Rayet (WR) stars based on IR colors and positional attributes. For our study, we choose an extensive data set of 6555 stellar objects (from 2MASS and AllWISE data releases) lying in the Milky Way (MW) with available photometric magnitudes of different types, including WR stars. Our XGB classifier model can accurately (with an 86% detection rate) identify a sufficient number of WR stars against a large sample of non-WR sources. The X

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A. Alharbi, Lubna. "Heart Disease Prediction of Cleveland Clinic Patients using Advanced Machine Learning Algorithms." Journal of Advanced Research Design 126, no. 1 (2025): 1–14. https://doi.org/10.37934/ard.126.1.114.

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Globally, cardiovascular diseases (CVDs) constitute the primary cause of morbidity and mortality worldwide. Early diagnosis of those at risk of CVDs may lower the number of avoidable fatalities. It has been shown that machine learning (ML) is helpful in anticipating cardiac issues. Adoption of a prediction system that can detect cardiac diseases before they deteriorate would offer people worldwide enormous hope and help in decision-making. ML has become a popular technique for generating predictions from enormous real-world datasets. It has also been discovered that many ML classifiers contain

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Afolabi, Hassan A., and Aburas A. Abdurazzag. "Statistical performance assessment of supervised machine learning algorithms for intrusion detection system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 266. http://dx.doi.org/10.11591/ijai.v13.i1.pp266-277.

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<span lang="EN-US">Several studies have shown that an ensemble classifier's effectiveness is directly correlated with the diversity of its members. However, the algorithms used to build the base learners are one of the issues encountered when using a stacking ensemble. Given the number of options, choosing the best ones might be challenging. In this study, we selected some of the most extensively applied supervised machine learning algorithms and performed a performance evaluation in terms of well-known metrics and validation methods using two internet of things (IoT) intrusion detection

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Saleem, Muniba, Waqar Aslam, Muhammad Ikram Ullah Lali, Hafiz Tayyab Rauf, and Emad Abouel Nasr. "Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis." Diagnostics 13, no. 22 (2023): 3441. http://dx.doi.org/10.3390/diagnostics13223441.

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Thalassemia represents one of the most common genetic disorders worldwide, characterized by defects in hemoglobin synthesis. The affected individuals suffer from malfunctioning of one or more of the four globin genes, leading to chronic hemolytic anemia, an imbalance in the hemoglobin chain ratio, iron overload, and ineffective erythropoiesis. Despite the challenges posed by this condition, recent years have witnessed significant advancements in diagnosis, therapy, and transfusion support, significantly improving the prognosis for thalassemia patients. This research empirically evaluates the e

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Matyukira, Charles, and Paidamwoyo Mhangara. "Land Cover and Landscape Structural Changes Using Extreme Gradient Boosting Random Forest and Fragmentation Analysis." Remote Sensing 15, no. 23 (2023): 5520. http://dx.doi.org/10.3390/rs15235520.

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Land use and land cover change constitute a significant driver of land degradation worldwide, and machine-learning algorithms are providing new opportunities for effectively classifying land use and land cover changes over time. The aims of this study are threefold: Firstly, we aim to compare the accuracies of the parametric classifier Naïve Bayes with the non-parametric classifier Extreme Gradient Boosting Random Forest algorithm on the 2020 LULC dataset. Secondly, we quantify land use and land cover changes in the Cradle of Humankind from 1990 to 2020 using the Extreme Gradient Boosting Rand

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Abdualgalil, Bilal, Sajimon Abraham, and Waleed M. Ismael. "Early Diagnosis for Dengue Disease Prediction Using Efficient Machine Learning Techniques Based on Clinical Data." Journal of Robotics and Control (JRC) 3, no. 3 (2022): 257–68. http://dx.doi.org/10.18196/jrc.v3i3.14387.

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Dengue fever is a worldwide issue, especially in Yemen. Although early detection is critical to reducing dengue disease deaths, accurate dengue diagnosis requires a long time due to the numerous clinical examinations. Thus, this issue necessitates the development of a new diagnostic schema. The objective of this work is to develop a diagnostic model for the earlier diagnosis of dengue disease using Efficient Machine Learning Techniques (EMLT). This paper proposed prediction models for dengue disease based on EMLT. Five different efficient machine learning models, including K-Nearest Neighbor (

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Sungur, Mustafa, Aykut Aykaç, Mehmet Erhan Aydin, Ozer Celik, and Coskun Kaya. "Machine Learning-Based Prediction of Prostate Biopsy Necessity Using PSA, MRI, and Hematologic Parameters." Journal of Clinical Medicine 14, no. 1 (2024): 183. https://doi.org/10.3390/jcm14010183.

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Background: To establish a machine learning (ML) model for predicting prostate biopsy outcomes using prostate-specific antigen (PSA) values, multiparametric magnetic resonance imaging (mpMRI) findings, and hematologic parameters. Methods: The medical records of the patients who had undergone a prostate biopsy were evaluated. Laboratory findings, mpMRI findings, and prostate biopsy results were collected. Patients with benign prostate pathology were classified as Group 1, and those with prostate cancer (PCa) were classified as Group 2. The following ML algorithms were used to create the ML mode

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Choubey, Shruti Bhargava, T. Chitra, and J. Jasmine Hephzipah. "Big Data Mining for Chronic Disease Prediction using Principal Component Analysis and eXtreme Gradient Boosting." GK International Journal of Advanced Research in Engineering and Technology 1, no. 1 (2024): 1–11. http://dx.doi.org/10.34293/gkijaret.v1i1.2024.1.

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Big data mining has revised health care with advanced analytics running on huge data sets, thereby improving immensely in disease detection and management. In this paper, a new approach of chronic disease detection is proposed, which combines PCA-based feature selection with the XGB classifier. It focuses only on the most important features, making analysis more efficient and accurate. This technique projects complicated medical data, be it biomarkers or clinical parameters, into a lower dimension called principal components, capturing essential variability but discarding less important inform

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Rudini, Edwin, and Ferda Ernawan. "Prediction of Alzheimer's Dementia Using Soft Voting Ensemble Learning with Machine Learning." IJACI : International Journal of Advanced Computing and Informatics 1, no. 1 (2025): 48–55. https://doi.org/10.71129/ijaci.v1.i1.pp48-55.

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Alzheimer's dementia (AD) is a degenerative brain disease characterized by a decline in cognitive function and memory. Predicting AD is crucial for preventing the disease from becoming more severe. Machine learning algorithms can aid in the early prediction of AD. The aim of this study is to develop a predictive model with improved accuracy using ensemble learning methods and machine learning algorithms. The experiment used the Oasis Longitudinal dataset from Oasis Brains, which includes details of patients with and without AD. This study proposed a binary classification using an ensemble lear

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Yotsawat, Wirot, Pakaket Wattuya, and Anongnart Srivihok. "Improved credit scoring model using XGBoost with Bayesian hyper-parameter optimization." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5477. http://dx.doi.org/10.11591/ijece.v11i6.pp5477-5487.

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<span>Several credit-scoring models have been developed using ensemble classifiers in order to improve the accuracy of assessment. However, among the ensemble models, little consideration has been focused on the hyper-parameters tuning of base learners, although these are crucial to constructing ensemble models. This study proposes an improved credit scoring model based on the extreme gradient boosting (XGB) classifier using Bayesian hyper-parameters optimization (XGB-BO). The model comprises two steps. Firstly, data pre-processing is utilized to handle missing values and scale the data.

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Guru, Chinmayee, and Walaa Bajnaid. "Prediction of Customer Sentiment Based on Online Reviews Using Machine Learning Algorithms." International Journal of Data Science and Advanced Analytics 5, no. 5 (2023): 272–79. http://dx.doi.org/10.69511/ijdsaa.v5i5.200.

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Customer opinions and feedback play a pivotal role in enhancing business operations and decision-making processes. Sentiment analysis is a crucial technique used to decipher customer opinions from their feedback and thus provide valuable insights for businesses. However, analysing and understanding reviews is an intricate process and prone to be misleading if not conducted meticulously. This study aims to extract and classify customer emotions from e-commerce reviews of women’s clothing in terms of polarity of sentiment, enhancing sentiment analysis accuracy by means of machine learning (ML) c

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Wirot, Yotsawat, Wattuya Pakaket, and Srivihok Anongnart. "Improved credit scoring model using XGBoost with Bayesian hyper-parameter optimization." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5477–87. https://doi.org/10.11591/ijece.v11i6.pp5477-5487.

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Several credit-scoring models have been developed using ensemble classifiers in order to improve the accuracy of assessment. However, among the ensemble models, little consideration has been focused on the hyperparameters tuning of base learners, although these are crucial to constructing ensemble models. This study proposes an improved credit scoring model based on the extreme gradient boosting (XGB) classifier using Bayesian hyper-parameters optimization (XGB-BO). The model comprises two steps. Firstly, data pre-processing is utilized to handle missing values and scale the data. Secondly, Ba

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Alanazi, Abed, and Abdu Gumaei. "A Decision-Fusion-Based Ensemble Approach for Malicious Websites Detection." Applied Sciences 13, no. 18 (2023): 10260. http://dx.doi.org/10.3390/app131810260.

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Malicious websites detection is one of the cyber-security tasks that protects sensitive information such as credit card details and login credentials from attackers. Machine learning (ML)-based methods have been commonly used in several applications of cyber-security research. Although there are some methods and approaches proposed in the state-of-the-art studies, the advancement of the most effective solution is still of research interest and needs to be improved. Recently, decision fusion methods play an important role in improving the accuracy of ML methods. They are broadly classified base

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de Assis, Débora, and Paulo Cesar Cortez. "A Comparative Analysis of Glaucoma Feature Extraction and Classification Techniques in Fundus Images." Journal of Communication and Information Systems 38, no. 1 (2023): 47–60. http://dx.doi.org/10.14209/jcis.2023.6.

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Glaucoma is a chronic asymptomatic eye disease that, if not treated in the initial stages, can induce blindness. However, early detection and proper treatment can prevent vision loss. Therefore, this work aims to evaluate the identification of glaucoma by non-invasive methods in fundus images. Initially, we have extracted the characteristics of images from the REFUGE and ACRIMA databases through the descriptors: Local Binary Patterns (LBP), Oriented Gradient Histogram (HOG), Zernike moments, and statistical information after the application of the Gabor filter. Then, we are given these charact

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Setiadi, De Rosal Ignatius Moses, Kristiawan Nugroho, Ahmad Rofiqul Muslikh, Syahroni Wahyu Iriananda, and Arnold Adimabua Ojugo. "Integrating SMOTE-Tomek and Fusion Learning with XGBoost Meta-Learner for Robust Diabetes Recognition." Journal of Future Artificial Intelligence and Technologies 1, no. 1 (2024): 23–38. http://dx.doi.org/10.62411/faith.2024-11.

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This research aims to develop a robust diabetes classification method by integrating the Synthetic Minority Over-sampling Technique (SMOTE)-Tomek technique for data balancing and using a machine learning ensemble led by eXtreme Gradient Boosting (XGB) as a meta-learner. We propose an ensemble model that combines deep learning techniques such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) with XGB classifier as the base learner. The data used included the Pima Indians Diabetes and Iraqi Society Diabetes datasets, which were processed by missing

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Jamali, A., M. Mahdianpari, and İ. R. Karaş. "A COMPARISON OF TREE-BASED ALGORITHMS FOR COMPLEX WETLAND CLASSIFICATION USING THE GOOGLE EARTH ENGINE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W5-2021 (December 23, 2021): 313–19. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w5-2021-313-2021.

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Abstract. Wetlands are endangered ecosystems that are required to be systematically monitored. Wetlands have significant contributions to the well-being of human-being, fauna, and fungi. They provide vital services, including water storage, carbon sequestration, food security, and protecting the shorelines from floods. Remote sensing is preferred over the other conventional earth observation methods such as field surveying. It provides the necessary tools for the systematic and standardized method of large-scale wetland mapping. On the other hand, new cloud computing technologies for the stora

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Sun, Fei, Fang Fang, Run Wang, et al. "An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images." Sensors 20, no. 22 (2020): 6699. http://dx.doi.org/10.3390/s20226699.

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Imbalanced learning is a common problem in remote sensing imagery-based land-use and land-cover classifications. Imbalanced learning can lead to a reduction in classification accuracy and even the omission of the minority class. In this paper, an impartial semi-supervised learning strategy based on extreme gradient boosting (ISS-XGB) is proposed to classify very high resolution (VHR) images with imbalanced data. ISS-XGB solves multi-class classification by using several semi-supervised classifiers. It first employs multi-group unlabeled data to eliminate the imbalance of training samples and t

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Walavalkar, Praniket, Ansh Dasrapuria, Meghna Sarda, and Lynette Dmello. "A Token-based Approach to Detect Fraud in Ethereum Transactions." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 34–42. http://dx.doi.org/10.22214/ijraset.2024.59690.

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Abstract: As a consequence of mass unemployment being the byproduct of COVID-19, people around the world discovered investment in cryptocurrency as a means to tackle their declining financial condition. Subsequently, the prominence of Ethereum as a platform for crypto transactions also gave rise to fraudulent transactions. The need to detect these frauds exists even today. This study proposes a token-based approach to detect fraud in Ethereum transactions incorporating the ERC20 standard, by employing machine learning techniques. After cleaning and preprocessing of the dataset, the transaction

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Hassan, Ghassan Muslim, Abdu Gumaei, Abed Alanazi, and Samah M. Alzanin. "A Network Intrusion Detection Approach Using Extreme Gradient Boosting with Max-Depth Optimization and Feature Selection." International Journal of Interactive Mobile Technologies (iJIM) 17, no. 15 (2023): 120–34. http://dx.doi.org/10.3991/ijim.v17i15.37969.

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Network intrusion detection system (NIDS) has become a vital tool to protect information anddetect attacks in computer networks. The performance of NIDSs can be evaluated by the numberof detected attacks and false alarm rates. Machine learning (ML) methods are commonly usedfor developing intrusion detection systems and combating the rapid evolution in the pattern ofattacks. Although there are several methods proposed in the state-of-the-art, the development ofthe most effective method is still of research interest and needs to be developed. In this paper,we develop an optimized approach using

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Bitto, Abu Kowshir, Md Hasan Imam Bijoy, Md Shohel Arman, Imran Mahmud, Aka Das, and Joy Majumder. "Sentiment analysis from Bangladeshi food delivery startup based on user reviews using machine learning and deep learning." Bulletin of Electrical Engineering and Informatics 12, no. 4 (2023): 2282–91. http://dx.doi.org/10.11591/beei.v12i4.4135.

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Food delivery methods are at the top of the list in today's world. People's attitudes toward food delivery systems are usually influenced by food quality and delivery time. We did a sentiment analysis of consumer comments on the Facebook pages of Food Panda, HungryNaki, Pathao Food, and Shohoz Food, and data was acquired from these four sites’ remarks. In natural language processing (NLP) task, before the model was implemented, we went through a rigorous data pre-processing process that included stages like adding contractions, removing stop words, tokenizing, and more. Four supervised classif

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Bitto, Abu Kowshir, Md Hasan Imam Bijoy, Md Shohel Arman, Imran Mahmud, Aka Das, and Joy Majumder. "Sentiment analysis from Bangladeshi food delivery startup based on user reviews using machine learning and deep learning." Bulletin of Electrical Engineering and Informatics 12, no. 4 (2023): 2282–91. http://dx.doi.org/10.11591/eei.v12i4.4135.

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Food delivery methods are at the top of the list in today's world. People's attitudes toward food delivery systems are usually influenced by food quality and delivery time. We did a sentiment analysis of consumer comments on the Facebook pages of Food Panda, HungryNaki, Pathao Food, and Shohoz Food, and data was acquired from these four sites’ remarks. In natural language processing (NLP) task, before the model was implemented, we went through a rigorous data pre-processing process that included stages like adding contractions, removing stop words, tokenizing, and more. Four supervised classif

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Deepti, Singh Kshatriya, and Barde Snehlata. "Comparative analysis of machine learning approaches for sentiment analysis of students' online learning feedback during covid-19." i-manager's Journal on Information Technology 11, no. 3 (2022): 13. http://dx.doi.org/10.26634/jit.11.3.19093.

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Sentiment analysis aids in determining if a person's feelings are neutral, negative, or positive. Many machine learning and deep learning algorithms exist for assessing people's attitudes on various social media networks. Many researchers focused on students' emotional identification. The purpose of this paper is to analyze the sentiments of academic students regarding the online class experience conducted during the COVID-19 pandemic situation. For this work, the Term Frequency-Inverse Document Frequency (TF-IDF) model is used for the feature extraction and comparison of eight machine learnin

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S, Oviya Graselin, Arunprasath T, Pallikonda Rajasekaran M, Ramalakshmi R, Kottaimalai R, and Alex Michael Raj J. "Development of a machine learning model to classify polycystic ovarian syndrome." Technology and Health Care 33, no. 3 (2024): 1478–88. https://doi.org/10.1177/09287329241296357.

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Background One of the main causes of infertility among women nowadays is Polycystic Ovarian Syndrome, or PCOS. Objectives: A decision support strategy for supporting medical specialists through PCOS monitoring is presented in the suggested work. A feature selection model that is based on wrapper categorization is used in this work. The performance of the classifier may be impacted by the existence of redundant features. Method: In order to address these issues, the PCOS is classified using the Machine Learning (ML) Extreme Gradient Boosting (XGBoost) classifier, while the best features are fou

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Khajanchi, Yukti, Monoo John, Durgesh Wadhwa, and Rupal Gupta. "Novel machine learning technique for analyzing personalities in status updates on facebook." Multidisciplinary Science Journal 6 (July 26, 2024): 2024ss0502. http://dx.doi.org/10.31893/multiscience.2024ss0502.

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The largest and widely used online social network, Facebook keeps extensive records of user activity as it is expressed in a variety of activities, including Facebook likes, status updates, posts, comments, photographs, tags, and shares. A social media user's tweets and other actions make their psychological foundations quite evident. However, making a prediction about this is difficult. This study investigates how to predict the HEXACO Model Personality Scores, which offer a quantitative assessment of users' personality characteristics, using integrated random forest fused extreme gradient bo

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Rahman, Saifur, Muhammad Irfan, Mohsin Raza, Khawaja Moyeezullah Ghori, Shumayla Yaqoob, and Muhammad Awais. "Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living." International Journal of Environmental Research and Public Health 17, no. 3 (2020): 1082. http://dx.doi.org/10.3390/ijerph17031082.

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Physical activity is essential for physical and mental health, and its absence is highly associated with severe health conditions and disorders. Therefore, tracking activities of daily living can help promote quality of life. Wearable sensors in this regard can provide a reliable and economical means of tracking such activities, and such sensors are readily available in smartphones and watches. This study is the first of its kind to develop a wearable sensor-based physical activity classification system using a special class of supervised machine learning approaches called boosting algorithms.

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Pinki, Farhana Tazmim, Md Abdul Awal, Khondoker Mirazul Mumenin, et al. "HGSOXGB: Hunger-Games-Search-Optimization-Based Framework to Predict the Need for ICU Admission for COVID-19 Patients Using eXtreme Gradient Boosting." Mathematics 11, no. 18 (2023): 3960. http://dx.doi.org/10.3390/math11183960.

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Millions of people died in the COVID-19 pandemic, which pressured hospitals and healthcare workers into keeping up with the speed and intensity of the outbreak, resulting in a scarcity of ICU beds for COVID-19 patients. Therefore, researchers have developed machine learning (ML) algorithms to assist in identifying patients at increased risk of requiring an ICU bed. However, many of these studies used state-of-the-art ML algorithms with arbitrary or default hyperparameters to control the learning process. Hyperparameter optimization is essential in enhancing the classification effectiveness and

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Raza, Ali, Furqan Rustam, Hafeez Ur Rehman Siddiqui, et al. "Predicting Genetic Disorder and Types of Disorder Using Chain Classifier Approach." Genes 14, no. 1 (2022): 71. http://dx.doi.org/10.3390/genes14010071.

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Genetic disorders are the result of mutation in the deoxyribonucleic acid (DNA) sequence which can be developed or inherited from parents. Such mutations may lead to fatal diseases such as Alzheimer’s, cancer, Hemochromatosis, etc. Recently, the use of artificial intelligence-based methods has shown superb success in the prediction and prognosis of different diseases. The potential of such methods can be utilized to predict genetic disorders at an early stage using the genome data for timely treatment. This study focuses on the multi-label multi-class problem and makes two major contributions

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Chowdhury, Nakib Hayat, Mamun Bin Ibne Reaz, Fahmida Haque, et al. "Performance Analysis of Conventional Machine Learning Algorithms for Identification of Chronic Kidney Disease in Type 1 Diabetes Mellitus Patients." Diagnostics 11, no. 12 (2021): 2267. http://dx.doi.org/10.3390/diagnostics11122267.

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Chronic kidney disease (CKD) is one of the severe side effects of type 1 diabetes mellitus (T1DM). However, the detection and diagnosis of CKD are often delayed because of its asymptomatic nature. In addition, patients often tend to bypass the traditional urine protein (urinary albumin)-based CKD detection test. Even though disease detection using machine learning (ML) is a well-established field of study, it is rarely used to diagnose CKD in T1DM patients. This research aimed to employ and evaluate several ML algorithms to develop models to quickly predict CKD in patients with T1DM using easi

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Jozdani, Shahab Eddin, Brian Alan Johnson, and Dongmei Chen. "Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification." Remote Sensing 11, no. 14 (2019): 1713. http://dx.doi.org/10.3390/rs11141713.

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With the advent of high-spatial resolution (HSR) satellite imagery, urban land use/land cover (LULC) mapping has become one of the most popular applications in remote sensing. Due to the importance of context information (e.g., size/shape/texture) for classifying urban LULC features, Geographic Object-Based Image Analysis (GEOBIA) techniques are commonly employed for mapping urban areas. Regardless of adopting a pixel- or object-based framework, the selection of a suitable classifier is of critical importance for urban mapping. The popularity of deep learning (DL) (or deep neural networks (DNN

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Edward, Jafhate, Marshima Mohd Rosli, and Ali Seman. "A Comprehensive Analysis of a Framework for Rebalancing Imbalanced Medical Data Using an Ensemble-based Classifier." Pertanika Journal of Science and Technology 32, no. 6 (2024): 2631–53. http://dx.doi.org/10.47836/pjst.32.6.12.

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In medical data, addressing imbalanced datasets is paramount for accurate predictive modeling. This paper delves into exploring a well-established rebalancing framework proposed in previous research. While acknowledged for its effectiveness, the adaptability of this framework across diverse medical datasets remains unexplored. We conduct a comprehensive investigation to bridge this gap by integrating an ensemble-based classifier into the existing framework. By leveraging seven imbalanced medical binary datasets, our study comprises three distinct experiments: utilizing standard baseline classi

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Wei, Jihu, Shijin Lu, Wencai Liu, et al. "A machine learning-based model for clinical prediction of distal metastasis in chondrosarcoma: a multicenter, retrospective study." PeerJ 11 (December 18, 2023): e16485. http://dx.doi.org/10.7717/peerj.16485.

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Background The occurrence of distant metastases (DM) limits the overall survival (OS) of patients with chondrosarcoma (CS). Early diagnosis and treatment of CS remains a great challenge in clinical practice. The aim of this study was to investigate metastatic factors and develop a risk stratification model for clinicians’ decision-making. Methods Six machine learning (ML) algorithms, including logistic regression (LR), plain Bayesian classifier (NBC), decision tree (DT), random forest (RF), gradient boosting machine (GBM) and extreme gradient boosting (XGBoost). A 10-fold cross-validation was

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Tariq, Muhammad Arham, Allah Bux Sargano, Muhammad Aksam Iftikhar, and Zulfiqar Habib. "Comparing Different Oversampling Methods in Predicting Multi-Class Educational Datasets Using Machine Learning Techniques." Cybernetics and Information Technologies 23, no. 4 (2023): 199–212. http://dx.doi.org/10.2478/cait-2023-0044.

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Abstract Predicting students’ academic performance is a critical research area, yet imbalanced educational datasets, characterized by unequal academic-level representation, present challenges for classifiers. While prior research has addressed the imbalance in binary-class datasets, this study focuses on multi-class datasets. A comparison of ten resampling methods (SMOTE, Adasyn, Distance SMOTE, BorderLineSMOTE, KmeansSMOTE, SVMSMOTE, LN SMOTE, MWSMOTE, Safe Level SMOTE, and SMOTETomek) is conducted alongside nine classification models: K-Nearest Neighbors (KNN), Linear Discriminant Analysis (

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Poonam, Moral, and Mustafi Debjani. "Improving Performance of Ensemble Learners for Breast Cancer Detection Using Feature Engineering." International Journal of Microsystems and IoT 1, no. 3 (2023): 148–55. https://doi.org/10.5281/zenodo.8354276.

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Machine learning (ML) approaches include a variety of statistical and probabilistic methodologies that enable intelligent systems to be trained from repeated prior knowledge to find and recognize interesting patterns. Breast cancer (BC) is a form of tumour that grows in the tissues of the breast, and it is the most recurrent kind of disease across the world and one of the major reasons for fatality in women. Early identification of breast cancer may raise the chance of successful therapy and lower the mortality rate. In this study, the effectiveness of various ensemble approaches for the autom

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Moataz, Mohamed El Sherbiny, Abdelhalim Eman, El-Din Mostafa Hossam, and Mohamed El-Seddik Mervat. "Classification ofchronic kidney disease based onmachine learning techniques." Classification ofchronic kidney disease based onmachine learning techniques 32, no. 2 (2023): 945–55. https://doi.org/10.11591/ijeecs.v32.i2.pp945-955.

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In recent years, indescribable suffering from various kidney diseases has been experienced by people all over the world. The situation has been significantly worse because of chronic kidney disease (CKD). Only through an early diagnosis of CKD may kidney disease be hinder in its early stages from progressing. However, it is easier to detect the chronic kidney disease with the aid of machine learning (ML) classifier algorithms sooner than any other existing methods. The present work proposes an approach for potentially predicting CKD infection while considering patient health dataset informatio

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Shyam, P. "Credit Card Fraud Detection Using Ensemble (Stacking and Voting Classifiers) with Hybrid Techniques." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 6555–65. https://doi.org/10.22214/ijraset.2025.71710.

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Credit card fraud remains a critical challenge in the financial industry due to the highly imbalanced nature of fraud detection datasets and the evolving tactics of fraudsters. This study proposes a robust framework for Credit Card Fraud Detection Using Ensemble (Stacking and Voting Classifiers) with Hybrid Techniques, integrating advanced resampling strategies with ensemble learning to enhance the detection of minority fraud cases.We evaluated various machine learning models combined with hybrid oversampling and undersampling methods, including Simple Minority Oversampling Technique(SMOTE)-To

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Gašparović, Mateo, and Dino Dobrinić. "Comparative Assessment of Machine Learning Methods for Urban Vegetation Mapping Using Multitemporal Sentinel-1 Imagery." Remote Sensing 12, no. 12 (2020): 1952. http://dx.doi.org/10.3390/rs12121952.

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Mapping of green vegetation in urban areas using remote sensing techniques can be used as a tool for integrated spatial planning to deal with urban challenges. In this context, multitemporal (MT) synthetic aperture radar (SAR) data have not been equally investigated, as compared to optical satellite data. This research compared various machine learning methods using single-date and MT Sentinel-1 (S1) imagery. The research was focused on vegetation mapping in urban areas across Europe. Urban vegetation was classified using six classifiers—random forests (RF), support vector machine (SVM), extre

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Becerra-Suarez, Fray L., Ismael Fernández-Roman, and Manuel G. Forero. "Improvement of Distributed Denial of Service Attack Detection through Machine Learning and Data Processing." Mathematics 12, no. 9 (2024): 1294. http://dx.doi.org/10.3390/math12091294.

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The early and accurate detection of Distributed Denial of Service (DDoS) attacks is a fundamental area of research to safeguard the integrity and functionality of organizations’ digital ecosystems. Despite the growing importance of neural networks in recent years, the use of classical techniques remains relevant due to their interpretability, speed, resource efficiency, and satisfactory performance. This article presents the results of a comparative analysis of six machine learning techniques, namely, Random Forest (RF), Decision Tree (DT), AdaBoost (ADA), Extreme Gradient Boosting (XGB), Mult

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Winkler, Simon, Benjamin Sackmann, Barbara Priwitzer, and Michael Lauxmann. "Institute of Technical Medicine (ITeM), Hochschule Furtwangen University (HFU), Germany." Current Directions in Biomedical Engineering 10, no. 4 (2024): 677–81. https://doi.org/10.1515/cdbme-2024-2166.

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Abstract Machine learning algorithms and neural networks have recently been used for the classification of middle ear disorders using wideband acoustic immittance and wideband tympanometry data. This study applies the extreme gradient boosting (XGB) classifier, trained on simulated WAI data, to classify real measured data for normal, otosclerotic, and disarticulated ears. The achieved macro recall of 82 % is comparable to other approaches trained with real measurement data. The interpretability methods LIME and SHAP are used to quantify each feature’s contribution, both revealing energy reflec

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Preeti. "An Optimized Ensemble IDS for DDoS Detection in IoT Environment." Journal of Electrical Systems 20, no. 11s (2024): 3802–11. https://doi.org/10.52783/jes.8266.

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Due to resource scarcity, efficient and accurate Intrusion Detection Systems (IDS) are crucial for safeguarding Internet of Things (IoT) settings from Distributed Denial of Service (DDoS) attacks. This study introduces an optimised ensemble (OE-IDS) framework that is both efficient and lightweight. It employs Recursive Feature Elimination (RFE) for feature selection and Principal Component Analysis (PCA) for feature extraction. The ensemble-based eXtreme Gradient Boosting (XGB) classifier outperforms conventional ensemble algorithms in detection rates when supplied with the appropriate informa

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Gašparović, Mateo, and Dino Dobrinić. "Green Infrastructure Mapping in Urban Areas Using Sentinel-1 Imagery." Croatian journal of forest engineering 42, no. 2 (2021): 337–56. http://dx.doi.org/10.5552/crojfe.2021.859.

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High temporal resolution of synthetic aperture radar (SAR) imagery (e.g., Sentinel-1 (S1) imagery) creates new possibilities for monitoring green vegetation in urban areas and generating land-cover classification (LCC) maps. This research evaluates how different pre-processing steps of SAR imagery affect classification accuracy. Machine learning (ML) methods were applied in three different study areas: random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB). Since the presence of the speckle noise in radar imagery is inevitable, different adaptive filters were exa

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Shweta Redkar. "Exploring Impact of Protein Sequence Local Information to Predict Enzyme-Ligand Binding Residues Using Machine Learning." Journal of Electrical Systems 20, no. 7s (2024): 1330–39. http://dx.doi.org/10.52783/jes.3706.

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Enzymes are important for various biochemical reactions in living cells. In drug discovery and drug design, identifying small molecule binding residues in enzymes is a crucial step. Although, identifying ligand-binding residues using computational techniques are improving, accurate prediction remains difficult. Therefore, to address this problem, we used the sequence local information, i.e., sequence neighbors around the target residue, and transformed it into two ways. First, into a Chaos Game Representation (CGR) to form a feature vector and train with an Extreme Gradient Boosting classifier

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Shakir Baawi, Salwa, Farah Jawad Al-Ghanim, and Nisreen Ryadh Hamza. "Categorization of Celebrity Photos Based on Deep Machine Learning for Feature Extraction and Classification." Wasit Journal of Computer and Mathematics Science 4, no. 1 (2025): 1–16. https://doi.org/10.31185/wjcms.341.

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In feature extraction, images or videos are analyzed to identify facial features before pinpointing the exact target. Significant progress has been made in the realm of feature extraction through deep learning. With deep learning technologies, developers have created algorithms for facial analysis and recognition that enhance accuracy and effectiveness. Consequently, programmers and developers have started to implement facial recognition technology in a wide range of applications to address these challenges. The identification process is particularly difficult due to the abundance of unstructu

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El Sherbiny, Moataz Mohamed, Eman Abdelhalim, Hossam El-Din Mostafa, and Mervat Mohamed El-Seddik. "Classification of chronic kidney disease based on machine learning techniques." Indonesian Journal of Electrical Engineering and Computer Science 32, no. 2 (2023): 945. http://dx.doi.org/10.11591/ijeecs.v32.i2.pp945-955.

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<span>In recent years, indescribable suffering from various kidney diseases has been experienced by people all over the world. The situation has been significantly worse because of chronic kidney disease (CKD). Only through an early diagnosis of CKD may kidney disease be hinder in its early stages from progressing. However, it is easier to detect the chronic kidney disease with the aid of machine learning (ML) classifier algorithms sooner than any other existing methods. The present work proposes an approach for potentially predicting CKD infection while considering patient health datase

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Faysal, Jabed Al, Sk Tahmid Mostafa, Jannatul Sultana Tamanna, et al. "XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection." Telecom 3, no. 1 (2022): 52–69. http://dx.doi.org/10.3390/telecom3010003.

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In the past few years, Internet of Things (IoT) devices have evolved faster and the use of these devices is exceedingly increasing to make our daily activities easier than ever. However, numerous security flaws persist on IoT devices due to the fact that the majority of them lack the memory and computing resources necessary for adequate security operations. As a result, IoT devices are affected by a variety of attacks. A single attack on network systems or devices can lead to significant damages in data security and privacy. However, machine-learning techniques can be applied to detect IoT att

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