CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection

In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a w...

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Veröffentlicht in:Scientific reports Jg. 14; H. 1; S. 701 - 18
Hauptverfasser: Abdelrazek, Mohammed, Abd Elaziz, Mohamed, El-Baz, A. H.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 06.01.2024
Nature Publishing Group
Nature Portfolio
Schlagworte:
639/705/117
639/705/794
Algorithms
Benchmarks
Feature selection
Foraging behavior
Gene mapping
Humanities and Social Sciences
multidisciplinary
Optimization algorithms
Science
Science (multidisciplinary)
ISSN:2045-2322, 2045-2322
Online-Zugang:Volltext
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Zusammenfassung:In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9–100%), sensitivity (77.6–100%), precision (91.8–96.08%), specificity (91.6–100%) and F-Score (90–100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC’2022 benchmarks functions.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-50959-8