Parameter settings. by Olaide N. Oyelade (14047002)

“…In addition, we designed nested transfer (NT) functions and investigated the influence of the function on the level-1 optimizer. The binary Ebola optimization search algorithm (BEOSA) is applied for the level-1 mutation, while the simulated annealing (SA) and firefly (FFA) algorithms are investigated for the level-2 optimizer. …”

<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

“…<p><b>(A)</b> Input data is a list of genotype-level SNPs. <b>(B)</b> Focusing on the positive class only, the algorithm exploits FIM (<i>apriori</i> algorithm) to build a list of candidate interactions of any desired order, retaining those that have an empirical frequency above a given threshold <i>δ</i>. …”

Feature selection convergence time comparison of various binary metaheuristic algorithms. by Amal H. Alharbi (21755906)

The result of the accuracy of tested algorithms. by Nebojsa Bacanin (13932723)

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Feature selection ratio results for tested algorithms. by Nebojsa Bacanin (13932723)

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Best fitness value results for tested algorithms. by Nebojsa Bacanin (13932723)

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Mean fitness value results for tested algorithms. by Nebojsa Bacanin (13932723)

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Standard deviation of fitness value results for tested algorithms. by Nebojsa Bacanin (13932723)

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a) Accuracy and b) selected feature size of algorithms on the COVID-19 dataset. by Nebojsa Bacanin (13932723)

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Boxplots analysis of the tested algorithms using average error rate across 21 datasets. by Nebojsa Bacanin (13932723)

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Control parameter settings for comparative feature selection methods. by Nebojsa Bacanin (13932723)

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Parameter settings of the comparison algorithms. by Ying Li (38224)

Binary contingency table. by Lu Xiao (314030)

S1 Dataset - Novel chaotic oppositional fruit fly optimization algorithm for feature selection applied on COVID 19 patients’ health prediction by Nebojsa Bacanin (13932723)

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The result of the best fitness value, mean fitness value, standard deviation of fitness value and feature selection ratio of algorithms on the COVID-19 dataset. by Nebojsa Bacanin (13932723)

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CEC 2019 benchmark characteristics. by Nebojsa Bacanin (13932723)

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Results of Holm’s step-down procedure. by Nebojsa Bacanin (13932723)

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Friedman ranks of tested methods on CEC2019 benchmark functions. by Nebojsa Bacanin (13932723)

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V-shaped transfer functions. by Nebojsa Bacanin (13932723)

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Resilts of Friedman and Iman-Davenport tests (<i>α</i> = 0.05). by Nebojsa Bacanin (13932723)

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