Innovative Feature Selection Method Based on Hybrid Sine Cosine and Dipper Throated Optimization Algorithms

Introduction: In pattern recognition and data mining, feature selection is one of the most crucial tasks. To increase the efficacy of classification algorithms, it is necessary to identify the most relevant subset of features in a given domain. This means that the feature selection challenge can be...

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Vydané v:	IEEE access Ročník 11; s. 79750 - 79776
Hlavní autori:	Abdelhamid, Abdelaziz A., El-Kenawy, El-Sayed M., Ibrahim, Abdelhameed, Eid, Marwa Metwally, Khafaga, Doaa Sami, Alhussan, Amel Ali, Mirjalili, Seyedali, Khodadadi, Nima, Lim, Wei Hong, Shams, Mahmoud Y.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Classification algorithms Data mining dipper throated optimization algorithm Feature extraction Feature selection Genetic algorithms Heuristic methods K-nearest neighbors algorithm Machine learning Mathematical models meta-heuristic optimization Metaheuristics Optimization Optimization algorithms Particle swarm optimization Pattern recognition Sine cosine optimization algorithm Stability analysis Trigonometric functions
ISSN:	2169-3536, 2169-3536
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