Feature importance of selected predictors by genetic algorithm. by Taiwo A. Adekunle (22226868)

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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.…”

In Silico Sorghum GxExM Dataset For Prediction Algorithm Comparisons by Owen Powell (16328658)

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Diagram of BILSTM-AEAM algorithm structure. by Hao Cao (2248066)

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The average overall prediction accuracy of different algorithms in gm measurement. by JiaMing Gong (20427837)

The average overall prediction accuracy of different algorithms in AUC measurement. by JiaMing Gong (20427837)

The average overall prediction accuracy of different algorithms in recall measurement. by JiaMing Gong (20427837)

The ANFIS algorithm details. by Mohammadmahdi Taheri (21722285)

“…These selected stocks are then moved to the second stage, where the ANFIS algorithm is employed in MATLAB to predict the final closing prices and calculate the prediction error (RMSE). …”

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. …”

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

The algorithm of the applied models. by Elif Tuçe Bal (21367965)

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Improved random forest algorithm. by Zhen Zhao (159931)

“…Subsequently, the feature factors corresponding to the model with the highest accuracy were selected as the optimal feature subsets and used in the model construction as input data. …”

K-means++ clustering algorithm. by Zhen Zhao (159931)

“…Subsequently, the feature factors corresponding to the model with the highest accuracy were selected as the optimal feature subsets and used in the model construction as input data. …”

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. …”

Algorithm framework. by Zongjin Li (38031)

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Algorithm performance comparison. by Haiyan Wang (25821)

Performance of models by different algorithms. by Yong Zhang (5893)

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Schematic diagram of TSO-VMD-BILSTM-AEAM algorithm structure. by Hao Cao (2248066)

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General architecture of XGBoost algorithm. by Saghir Ahmed (4416544)

Comparative analysis of off-policy PPO algorithm and on-policy algorithm on the a) CSMAR and b) MorningStar datasets. by Cai Yuanqing (22513351)