<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>A novel proposed Binary Volleyball Premier League algorithm (BVPL) has shown some promising results in a Parkinson’s Disease (PD) dataset related to fitness and accuracy [<xref ref-type="bibr" rid="j_cait-2023-0038_ref_001">1</xref>]. This paper evaluates and provides an overview of the efficiency of BVPL in feature selection compared to various metaheuristic optimization algorithms and PD datasets. Moreover, an improved variant of BVPL is proposed that integrates the opposite-based solution to enlarge search domains and increase the possibility of getting rid of the local optima. The performance of BVPL is validated using the accuracy of the k-Nearest Neighbor Algorithm. The superiority of BVPL over the competing algorithms for each dataset is measured using statistical tests. The conclusive results indicate that the BVPL exhibits significant competitiveness compared to most metaheuristic algorithms, thereby establishing its potential for accurate prediction of PD. Overall, BVPL shows high potential to be employed in feature selection.</p>
</abstract>

A novel proposed Binary Volleyball Premier League algorithm (BVPL) has shown some promising results in a Parkinson’s Disease (PD) dataset related to fitness and accuracy [1]. This paper evaluates and provides an overview of the efficiency of BVPL in feature selection compared to various metaheuristic optimization algorithms and PD datasets. Moreover, an improved variant of BVPL is proposed that integrates the opposite-based solution to enlarge search domains and increase the possibility of getting rid of the local optima. The performance of BVPL is validated using the accuracy of the k-Nearest Neighbor Algorithm. The superiority of BVPL over the competing algorithms for each dataset is measured using statistical tests. The conclusive results indicate that the BVPL exhibits significant competitiveness compared to most metaheuristic algorithms, thereby establishing its potential for accurate prediction of PD. Overall, BVPL shows high potential to be employed in feature selection.

A Competitive Parkinson-Based Binary Volleyball Premier League Metaheuristic Algorithm for Feature Selection

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A novel proposed Binary Volleyball Premier League algorithm (BVPL) has shown some promising results in a Parkinson’s Disease (PD) dataset related to fitness...

A Competitive Parkinson-Based Binary Volleyball Premier League Metaheuristic Algorithm for Feature Selection

Online veröffentlicht: 30. Nov. 2023

Seitenbereich: 91 - 109

DOI: https://doi.org/10.2478/cait-2023-0038

Schlüsselwörter
Binary Volleyball Premier League, Feature Selection (FS), k-Nearest Neighbor (kNN), Parkinson’s Disease (PD), MetaHeuristic optimization Algorithms (MHA)

© 2023 Edjola Naka, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Competitive Parkinson-Based Binary Volleyball Premier League Metaheuristic Algorithm for Feature Selection

Online veröffentlicht: 30. Nov. 2023

Seitenbereich: 91 - 109

DOI: https://doi.org/10.2478/cait-2023-0038

SchlüsselwörterBinary Volleyball Premier League, Feature Selection (FS), k-Nearest Neighbor (kNN), Parkinson’s Disease (PD), MetaHeuristic optimization Algorithms (MHA)

© 2023 Edjola Naka, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Binary Volleyball Premier League, Feature Selection (FS), k-Nearest Neighbor (kNN), Parkinson’s Disease (PD), MetaHeuristic optimization Algorithms (MHA)