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

Immune infiltration analysis of the prognostic model in LIHC using multiple algorithms. by Shuang Zheng (768179)

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

Subjects:

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

Performance of HFKG (triplet selection) on multiple clients divided by different datasets. by Chunjuan Li (7890182)

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

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.…”

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

Evaluation metrics of selected ML models. by Gourab Saha (8987405)

Subjects:

The results of fitting a phenomenological spiking neuronal model (the adaptive exponential integrate-and-fire model) to capture experimental recordings with multiple traces. by Máté Mohácsi (20469514)

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

Subjects:

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. …”

Immune infiltration analysis by CIBERSORT algorithm. by Lanying Shen (22755930)

Immune infiltration analysis by CIBERSORT algorithm. by Lanying Shen (22755930)

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. …”

Run times of two algorithms. by Gourab Saha (8987405)

Subjects:

Comparative overview of multi-objective optimization algorithms. by Xiaodan Li (132227)

Actual vs predicted net demand for all three models. by Mohd Sakib (20565228)

Subjects:

Overall workflow diagram of the proposed model. by Mohd Sakib (20565228)

Subjects:

AUC scores of anomaly detection algorithms. by GaoXiang Zhao (21499525)

“…Under the framework of model averaging, this paper proposes a criterion for the selection of weights in the aggregation of multiple models, employing a focal loss function with Mallows’ form to assign weights to the base models. …”