Data and code resources. by Sam Hall-McMaster (10343795)

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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. …”

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.…”

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. …”

Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code by Goutami Sooda (20714729)

Variable selection procedure using the Boruta algorithm. by Qiudie Liu (22655907)

“…<p>Variable selection procedure using the Boruta algorithm.</p>…”

Feature selection using the Boruta algorithm method. by Alemu Birara Zemariam (17540938)

Code used for data analysis in this study. by Nourin Sultana (22339463)

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Participant selection algorithm. by Alex Gough (16734033)

Participant selection algorithm. by Alex Gough (16734033)

The selection algorithm flowchart. by Shiyu Wang (813972)

GA pseudo-code. by Jianpeng Zhang (528185)

“…GA is used to optimize the feature selection process to identify the key feature subsets that have the greatest impact on model performance. …”

Patient selection algorithm. by Dwi Kisworo Setyowireni (22656144)

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

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List of the time used by each algorithm. by JiaMing Gong (20427837)

“…In this manner high quality and common samples are randomly selected for training the classifier. Finally, to solve the issue of concept drift, EDAC designs and implements an ensemble classifier that uses a self-feedback strategy to determine the initial weight of the classifier by adjusting the weight of the sub-classifier according to the performance on the arrived data chunks. …”

Feature selection results from the Boruta algorithm. by Junying Wei (1710550)

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Scenarios for algorithms using IEEE 33 bus. by Bachirou Bogno (20485724)

Scenarios for algorithms using IEEE 69 bus. by Bachirou Bogno (20485724)

TIR-Learner v3: New generation TE annotation program for identifying TIRs by Tianyu Lu (18856792)

“…The old TIR suffers from slow execution on large genomes due to intense I/O operations and less efficient algorithms, it also lacks maintainability due to legacy dependency issues. …”

Steps involved in genetic algorithm for feature selection. by Mohd Sakib (20565228)

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