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

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

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Details of multiple GWO algorithm variants. by Genliang Li (5816264)

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.…”

IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

“…To address this problem, this paper proposes an improved red-billed blue magpie algorithm (IRBMO), which is specifically optimized for the feature selection task, and significantly improves the performance and efficiency of the algorithm on medical data by introducing multiple innovative behavioral strategies. …”

Performance comparison of ML algorithms across multiple evaluation metrics. by Muhammad Tausif (12385892)

Box plot the selected feature for weekends and weekdays in the dataset. by Mohd Sakib (20565228)

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The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

“…To address this problem, this paper proposes an improved red-billed blue magpie algorithm (IRBMO), which is specifically optimized for the feature selection task, and significantly improves the performance and efficiency of the algorithm on medical data by introducing multiple innovative behavioral strategies. …”

IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

“…To address this problem, this paper proposes an improved red-billed blue magpie algorithm (IRBMO), which is specifically optimized for the feature selection task, and significantly improves the performance and efficiency of the algorithm on medical data by introducing multiple innovative behavioral strategies. …”

Standard model selected for research. by Guangmei Ma (21432861)

Average accuracy by feature selection method. by Şafak Kılıç (22227019)

“…</p><p>Materials and methods:</p><p>We propose a hybrid architecture integrating ResNet50 backbone with CBAM attention mechanisms, enhanced by a comprehensive deep feature engineering pipeline. The framework incorporates multiple feature extraction layers (CBAM, GAP, GMP, pre-final) combined with 10 distinct feature selection methods including Principal Component Analysis (PCA), Chi-square test, Random Forest importance, variance thresholding, and their intersections. …”

Fitness curves for each algorithm. by Minan Tang (20389941)

“…Then, the CPO-CNN classification model is used for feature extraction and feature selection of the time-frequency diagrams and classification of multiple power quality disturbances. …”

Normalized results of significant frame difference features. by Yandong Liu (11893664)

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Histogram of accuracy values for different machine learning models across multiple runs. by Amal H. Alharbi (21755906)

Fusion outcomes for select frames in the INO dataset. by Yandong Liu (11893664)

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Fusion outcomes for select frames in the BEPMS dataset. by Yandong Liu (11893664)

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Fusion outcomes for select frames in the OTCBVS dataset. by Yandong Liu (11893664)

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Time-frequency feature extraction flowchart. by Minan Tang (20389941)

“…Then, the CPO-CNN classification model is used for feature extraction and feature selection of the time-frequency diagrams and classification of multiple power quality disturbances. …”

Algorithm flow of the IFE method. by Jia Liu (41593)

“…For this issue, we proposed a new sensor array optimization algorithm named Interfered Feature Elimination coupled with Feature Group Selection (IFE-FGS). …”

Model results with temporal features. by Qun Zhao (603175)