An Intelligent Dynamic MRI System for Automatic Nasal Tumor Detection by Wen-Chen Huang, Chun-Liang Liu

“…These three classifiers are AdaBoost, SVM, and Bayes-Gaussian classifier. …”
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Multi-convolutional neural networks for cotton disease detection using synergistic deep learning paradigm. by Afira Aslam, Syed Muhammad Usman, Muhammad Zubair, Amanullah Yasin, Muhammad Owais, Irfan Hussain

“…After preprocessing, we combine features extracted from MobileNet and VGG16 to create a comprehensive feature vector, passed to three classifiers: Long Short Term Memory Units, Support Vector Machines, and Random Forest. …”
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Traffic Road Sign Detection and Classification by Mehdi Fartaj, Sedigheh Ghofrani

“…For this purpose, we employ and compare the performance of three classifiers; they are distance to border (DTB), FFT sample of signature, and code matrix. …”
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Linear Dimensionality Reduction: What Is Better? by Mohit Baliyan, Evgeny M. Mirkes

“…This study uses 22 classification datasets and three classifiers, namely Fisher’s discriminant classifier, logistic regression, and K nearest neighbors, to test the effectiveness of the three heuristics. …”
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Hybridization of DEBOHID with ENN algorithm for highly imbalanced datasets by Sedat Korkmaz

“…A parameter analysis was conducted on D-ENN to determine the optimal values for the F, CR and D-ENN-Type parameters. Three classifiers were used in the study: Support Vector Machines (SVM), Decision Tree (DT), and K-nearest Neighbor (kNN), and reported their G-mean and Area Under Curve (AUC) values. …”
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Individual Identification Using Radar-Measured Respiratory and Heartbeat Features by Haruto Kobayashi, Yuji Tanaka, Takuya Sakamoto

“…To identify a suitable combination of features and a classifier, we compare the performances of nine methods based on various combinations of three feature vectors with three classifiers. The accuracy of the proposed method in performing individual identification is evaluated using a 79-GHz millimeter-wave radar system with an antenna array in two experimental scenarios and we demonstrate the importance of use of the combination of the respiratory and heartbeat features in achieving accurate identification of individuals. …”
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The Use of Hellinger Distance Undersampling Model to Improve the Classification of Disease Class in Imbalanced Medical Datasets by Zina Z. R. Al-Shamaa, Sefer Kurnaz, Adil Deniz Duru, Nadia Peppa, Alex H. Mirnezami, Zaed Z. R. Hamady

“…An extensive experiment has been conducted on four imbalanced medical datasets using three classifiers to compare HDUS with a baseline model and three state-of-the-art undersampling models. …”
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A Hybrid Method of Linguistic and Statistical Features for Arabic Sentiment Analysis by Ahmed Sabah AL-Jumaili

“…A benchmark dataset of Arabic tweets have been used in the experiments. In addition, three classifiers have been utilized including SVM, KNN and ME. …”
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An efficient and interactive feature selection approach based on copula entropy for high-dimensional genetic data by Xiaoran Yan, Shilong Shang, Dongxi Li, Yun Dang

“…Finally, we evaluate the effectiveness of CEFS and CEFS+ using three classifiers on five datasets. In 10 out of 15 scenarios, our approach obtains the highest classification accuracy, which is much higher than the other six commonly used FS methods. …”
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Bearing Fault Detection Using Multi-Scale Fractal Dimensions Based on Morphological Covers by Pei-Lin Zhang, Bing Li, Shuang-Shan Mi, Ying-Tang Zhang, Dong-Sheng Liu

“…The MFDs can provide more discriminative information about the signals than the single global fractal dimension. Furthermore, three classifiers are employed to evaluate and compare the classification performance of the MFDs with other feature extraction methods. …”
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Evaluating structural safety of trusses using Machine Learning by Tran-Hieu Nguyen, Anh-Tuan Vu

“…For the two first examples, three classifiers get more than 90% of accuracy. For the 47-bar truss, the accuracies of the Support Vector Machine model and the Deep Neural Network model are lower than 70% but the Adaptive Boosting model still retains the high accuracy of approximately 98%. …”
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A composite Feature Selection Method to improve Classifying Imbalanced Big Data by Shaymaa Razoqi, Ghayda Al-Talib

“…Therefore, this research proposed a composed feature selection method using the filter feature selection technique and permutation-based important features with the ensemble learning method. Three classifiers were used with three performance metrics (AUC, G-means, and F-score ) to show the effect of proposed feature selection method with imbalanced big data. …”
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Improving ridership by predicting train occupancy levels by Muhammad Awais Shafique

“…Similarly, 1–5 days of known occupancy levels are added to each data instance. Among the three classifiers used, XGBoost provides the best results. …”
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Acute lymphoblastic leukemia diagnosis using machine learning techniques based on selected features by Enas M.F. El Houby

“…The proposed system was applied to microscopic blood images to classify each case as ALL or normal. Three classifiers which are Naïve Bayes (NB), Support Vector Machine (SVM) and K-nearest Neighbor (K-NN) were utilized to classify the images based on selected features. …”
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Maturity Classification and Quality Determination of Cherry Using VNIR Hyperspectral Images and Comprehensive Chemometrics by Yuzhen Wei, Siyi Yao, Feiyue Wu, Qiangguo Yu

“…Based on the spectral principal components, three classifiers were built to classify the maturity level: support vector machine, backpropagation neural network, and radial basis function neural network. …”
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Enhanced Forecasting of Equity Fund Returns Using Machine Learning by Fabiano Fernandes Bargos, Estaner Claro Romão

“…Based on cross-validated accuracy, we focused on the top three classifiers. As a result, the developed models achieved accuracy, recall, and precision values exceeding 0.92 on the test data. …”
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Developing a Machine Vision System to Detect Weeds from Potato Plant by Yousef Abbaspour Gılandeh, Hossein Javadıkıa, Sajad Sabzı

“…The correct classification accuracy for testing and training data using three classifiers of the hybrid ANN-ACO, radial basis function (RBF) artificial neural network, and Discriminant analysis (DA) was 99.6% and 98.13%, 97.24% and 91.23%, and 69.8% and 70.8%, respectively. …”
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A Study on CNN-Based and Handcrafted Extraction Methods with Machine Learning for Automated Classification of Breast Tumors from Ultrasound Images by Mohamed Benaouali, Mohamed Bentoumi, Mansour Abed, Malika Mimi, Abdelmalik Taleb-Ahmed

“…For classification, we explored three classifiers: linear support vector machines (SVM), k-nearest neighbors (KNN), and artificial neural networks (ANN). …”
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A real-time predicting online tool for detection of people’s emotions from Arabic tweets based on big data platforms by Naglaa Abdelhady, Ibrahim E. Elsemman, Taysir Hassan A. Soliman

“…For the first stage, two different approaches: The deep Learning (DL) approach and the Transfer Learning-based (TL) approach to find the optimal classifier for identifying and predicting emotion. For DL, three classifiers are applied: Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). …”
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Radiation hematologic toxicity prediction in rectal cancer: a comparative radiomics-based study on CT image and dose map by Yingpeng Liu, Liping Guo, Yi Wang, Qingtao Xu, Jingfeng Zhang, Xianyun Meng

“…Then, the radiomic features of the clinical target volume (CTV) in the radiotherapy were extracted, and the least absolute shrinkage and selection operator (LASSO) algorithm was used for feature dimension deduction; three classifiers, that is, support vector machine (SVM) (rbf kernel), random forest, and CatBoost, were used to construct the HT classification model in rectal cancer patients. …”
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