Diagnoses which were not selected by either algorithm across the three cohorts. by Mohammad A. Al-Mamun (6086642)

“…<p>Diagnoses which were not selected by either algorithm across the three cohorts.…”

Core genes were selected through PPI analysis based on three algorithms. by Dong-Hee Han (140305)

“…<p>Among 46 DEGs whose expression significantly changed in A549 and BEAS-2B cell lines, the core genes were selected using three algorithms: MCC, MNC, and DEGREE. …”

Three machine learning algorithms were further employed to select candidate key hub genes. by Zhifeng Wu (303016)

“…(C, D) The Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithm was used to select significant feature genes from the 20 candidate genes. …”

The selection algorithm flowchart. by Shiyu Wang (813972)

Participant selection algorithm. by Alex Gough (16734033)

Patient selection algorithm. by Dwi Kisworo Setyowireni (22656144)

Participant selection algorithm. by Alex Gough (16734033)

Summary of the feature selection algorithms. by Martin Kostadinov (20410120)

Feature selection using the Boruta algorithm. by Guang Tu (22054865)

“…We extracted baseline characteristics, laboratory parameters, and clinical outcomes. The Boruta algorithm was employed for feature selection to identify variables significantly associated with in-hospital mortality, and 16 machine learning models, including logistic regression, random forest, gradient boosting, and neural networks, were developed and compared using receiver operating characteristic (ROC) curves and area under the curve (AUC) analysis. …”

Features selection using the Boruta algorithm. by Nishat Tasnim Thity (21755858)

“…We identified the important features related to IA using the Boruta algorithm. Predictions were made using different machine learning (ML) (decision tree (DT), random forest (RF), support vector machines (SVMs), and logistic regression (LR)) models. …”

Proposed genetic algorithm for feature selection. by Shirin Dehghan (19837936)

“…Using GA, Random Forest also demonstrated strong performance, achieving an accuracy rate of 87.4%. Genetic Algorithm significantly improved the performance of all classifiers, emphasizing the importance of feature selection. …”

Feature selection using Boruta algorithm. by Shayla Naznin (13014015)

“…Feature selection was performed using the Boruta algorithm and model performance was evaluated by comparing accuracy, precision, recall, F1 score, MCC, Cohen’s Kappa and AUROC.…”

Choices on individual test tasks were not explained by model-free perseveration. by Sam Hall-McMaster (10343795)

Ten key risk factors for C-section delivery were identified by three feature selection techniques. by Shamsuz Zoha (22339469)

Parameters of ML algorithms. by Md. Maeen Molla (20827041)

The trace plot of Genetic Algorithm. by Zeonlung Pun (21395508)

“…By employing SHAP (SHapley Additive exPlanations) and LassoNet, we identified and refined 50 critical molecular descriptors from an initial set of 729, significantly influencing the prediction of bioactivity. The selected descriptors were systematically validated, bolstering the predictive robustness of our models, which demonstrated a mean coefficient of determination of 77 for bioactivity and high accuracy scores of 90.2, 93.7, 89.5, 87.3, and 95.8 for absorption, distribution, metabolism, excretion, and toxicity, respectively. …”

Feature selection process. by Balraj Preet Kaur (20370832)

“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

Flowchart of selected guidelines. by Margaretha G. M. Roemer (9857054)

“…Quality of evidence underlying cut-offs and parameters was variable, and laboratory aspects were underrepresented.</p><p>Conclusions</p><p>There was a lot of variation in the included diagnostic algorithms, especially for iron deficiency anaemia. …”

Training and validation loss curves for eight different splits that were generated using the VessMAP metadata. by Matheus Viana da Silva (21433572)

Feature correspondence position selection. by Xiaotong Bai (19819284)

“…<div><p>Hybrid feature selection algorithm is a strategy that combines different feature selection methods aiming to overcome the limitations of a single feature selection method and improve the effectiveness and performance of feature selection. …”