Improved Feature-Selection Method Considering the Imbalance Problem in Text Categorization by Jieming Yang, Zhaoyang Qu, Zhiying Liu

“…We evaluated the improved versions of nine well-known feature-selection methods (Information Gain, Chi statistic, Document Frequency, Orthogonal Centroid Feature Selection, DIA association factor, Comprehensive Measurement Feature Selection, Deviation from Poisson Feature Selection, improved Gini index, and Mutual Information) using naïve Bayes and support vector machines on three benchmark document collections (20-Newsgroups, Reuters-21578, and WebKB). …”
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Predictive Modeling for Cardiovascular Disease in Patients Based on Demographic and Biometric Data by Abayomi Danlami Babalola, Kayode Francis Akingbade, Daniel Olakunle

“…This study explores the application of support vector machines (SVMs), ensemble learning, and artificial neural networks (NNs) for predictive modeling of CVD in patients. …”
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Predicting adherence to gamified cognitive training using early phase game performance data: Towards a just-in-time adherence promotion strategy. by Yuanying Pang, Ankita Singh, Shayok Chakraborty, Neil Charness, Walter R Boot, Zhe He

“…<h4>Research design and method</h4>Using machine learning algorithms including logistic regression, ridge regression, support vector machines, classification trees, and random forests, we predicted adherence from weeks 3 to 12. …”
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Estimating Self-Confidence in Video-Based Learning Using Eye-Tracking and Deep Neural Networks by Ankur Bhatt, Ko Watanabe, Jayasankar Santhosh, Andreas Dengel, Shoya Ishimaru

“…To assess the collected data, we compare three different algorithms: a Long Short-Term Memory (LSTM), a Support Vector Machine (SVM), and a Random Forest (RF), thereby providing a comprehensive evaluation of the data. …”
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Establishment of an MRI-based radiomics model for distinguishing between intramedullary spinal cord tumor and tumefactive demyelinating lesion by Zifeng Zhang, Ning Li, Yuhang Qian, Huilin Cheng

“…Ten classification algorithms were employed: logistic regression (LR); naive bayes (NaiveBayes); support vector machine (SVM); k nearest neighbors (KNN); random forest (RF); extra trees (ExtraTrees); eXtreme gradient boosting (XGBoost); light gradient boosting machine (LightGBM); gradient boosting (GradientBoosting); and multi-Layer perceptron (MLP). …”
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An automated approach to identify sarcasm in low-resource language. by Shumaila Khan, Iqbal Qasim, Wahab Khan, Aurangzeb Khan, Javed Ali Khan, Ayman Qahmash, Yazeed Yasin Ghadi

“…The primary models evaluated in this study are support vector machine (SVM), decision tree (DT), K-Nearest Neighbor Classifier (K-NN), linear regression (LR), random forest (RF), Naïve Bayes (NB), and XGBoost. …”
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Sentiment analysis of pilgrims using CNN-LSTM deep learning approach by Aisha Alasmari, Norah Farooqi, Youseef Alotaibi

“…Our model is compared with a set of Machine Learning (ML) models including Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF), as well as CNN and LSTM models. …”
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Quadratic Regression Models for Profile Picture NFT Valuation by Geun-Cheol Lee, Hoon-Young Koo, Heejung Lee

“…For benchmarking purposes, we compare the proposed models against four machine learning algorithms: Random Forest, Support Vector Regression (SVR), XGBoost, and LightGBM. …”
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LncRNAs regulates cell death in osteosarcoma by Ping’an Zou, Zhiwei Tao, Zhengxu Yang, Tao Xiong, Zhi Deng, Qincan Chen, Li Niu

“…Univariate Cox regression analysis was employed to identify lncRNAs associated with osteosarcoma treatment. Three machine learning algorithms—Support Vector Machine, Random Forest, and Generalized Linear Model—were utilized to select feature genes. …”
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Application of federated learning in predicting breast cancer by Chai Jiarui

“…During the local training process, the data is normalized and feature extracted, initially classified using support vector machines (SVM) or penalized logistic regression and optimized using stochastic gradient descent (SGD). …”
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Enhancing land feature classification with the BTR Extractor: A novel software package for high-accuracy analysis of aerial laser scan data by Jamshid Talebi, Zahra Azizi

“…We employed five methods (Bayesian algorithms, support vector machine, K-nearest neighbor, C-Tree, and discriminant analysis) to classify land features. …”
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A Novel Feature Selection Method for Classification of Medical Data Using Filters, Wrappers, and Embedded Approaches by Saba Bashir, Irfan Ullah Khattak, Aihab Khan, Farhan Hassan Khan, Abdullah Gani, Muhammad Shiraz

“…Furthermore, classification is then performed on selected features to classify the data using a support vector machine (SVM) classifier. Two publically available benchmark datasets are used, i.e., the Microarray dataset and the Cleveland Heart Disease dataset, for experimentation and analysis, and they are archived from the UCI data repository. …”
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A Risk Warning Model for Anemia Based on Facial Visible Light Reflectance Spectroscopy: Cross-Sectional Study by Yahan Zhang, Yi Chun, Hongyuan Fu, Wen Jiao, Jizhang Bao, Tao Jiang, Longtao Cui, Xiaojuan Hu, Ji Cui, Xipeng Qiu, Liping Tu, Jiatuo Xu

“…ResultsThe study identified significant differences in facial spectral features between anemic patients and healthy controls. The support vector machine classifier outperformed other classification models, achieving an accuracy of 0.875 (95% CI 0.825-0.925) for distinguishing between the anemia and healthy control groups. …”
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Contact-Free Cognitive Load Recognition Based on Eye Movement by Xin Liu, Tong Chen, Guoqiang Xie, Guangyuan Liu

“…Finally we used the support vector machine (SVM) to classify high and low cognitive load. …”
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Wetland vegetation mapping improved by phenological leveraging of multitemporal nanosatellite images by Lucas T. Fromm, Laurence C. Smith, Ethan D. Kyzivat

“…Maximum Likelihood (MLC), Support Vector Machine (SVM), and Artificial Neural Network (ANN) classification algorithms are tested on individual, monthly- and multi-seasonal composite images. …”
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Statistical Learning-Based Spatial Downscaling Models for Precipitation Distribution by Yichen Wu, Zhihua Zhang, M. James C. Crabbe, Lipon Chandra Das

“…In this study, based on three statistical learning algorithms, such as support vector machine (SVM), random forest regression (RF), and gradient boosting regressor (GBR), we proposed an efficient downscaling approach to produce high spatial resolution precipitation. …”
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Automated Detection of Recent Mud Extrusions Using UAV Imagery and Deep Learning: A Comparative Analysis of Traditional and CNN-Based Approaches by M. Guastella, M. Guastella, A. Pisciotta, R. Martorana, A. D’Alessandro

“…A binary image classification pipeline was developed to distinguish recent mud from non-mud areas. Traditional machine learning algorithms, including Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost), were compared with deep learning architectures such as Convolutional Neural Networks (CNNs), both leveraging transfer learning and custom models. …”
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A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), na&#x00EF;ve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). …”
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Examining Car Accident Prediction Techniques and Road Traffic Congestion: A Comparative Analysis of Road Safety and Prevention of World Challenges in Low-Income and High-Income Cou... by Yetay Berhanu, Esayas Alemayehu, Dietrich Schröder

“…The study evaluates various approaches such as logistic regression, decision tree, random forest, deep neural network, support vector machine, random forest, K-nearest neighbors, Naïve Bayes, empirical Bayes, geospatial analysis methods, and UIMA, NSGA-II, and MOPS algorithms. …”
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Artificial intelligence for severity triage based on conversations in an emergency department in Korea by Jae Won Seo, Sung-Joon Park, Young Jae Kim, Jung-Youn Kim, Kwang Gi Kim, Young-Hoon Yoon

“…Based on the area under the receiver operating characteristic curve (AUROC) values, the support vector machine achieved the best performance among the term frequency-inverse document frequency-based conventional machine learning models with an AUROC of 0.764 (95% CI 0.019). …”
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