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

Algorithm performance comparison of single sequence features and multiple input sequence features. by Dong Yi (511609)

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

Subjects:

Summary of feature-selection algorithms. by Stephen R. Piccolo (8367780)

“…<p>We evaluated 14 feature-selection algorithms. The abbreviation for each algorithm contains a prefix that indicates which machine-learning library implemented the algorithm (mlr = Machine learning in R, sklearn = scikit-learn, weka = WEKA: The workbench for machine learning). …”

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.…”

Main steps used in multiple imputation [31]. by Maritza Mera-Gaona (11198145)

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. …”

Comparison of all ISD algorithms using MICE imputation and multivariate Cox feature selection. by Shi-ang Qi (14279265)

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)

Subjects:

Empirical distribution of selected features of the dataset. by Michał Tomaszewski (8198115)

Proposed methodology to select a feature needed to imputed first in imputation by feature importance (IBFI) method. by Adil Aslam Mir (11945234)

The comparison with machine learning algorithms. by Jiyun Yang (45626)

Design and implementation of the Multiple Criteria Decision Making (MCDM) algorithm for predicting the severity of COVID-19. by Jiaqing Luo (10975030)

Comparison of average recognition accuracy of different SEI algorithms under different input features. by Dong Yi (511609)

The AUCs of our method and machine learning algorithms. by Jiyun Yang (45626)

[Foga2021]-On the Potential of Normalized TSP Features for AAS by Janina Pohl (11051463)

“…Such artifacts may have detrimental effects on algorithm selection models.</div><div>We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) a k-nearest neighbor graph (NNG) transformation of the input instance. …”

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. …”

Top models based on analysis of training and testing set samples using a suite of ML algorithms with sequential feature exclusion based on feature predictive importance. by Cathal Meehan (14129680)