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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE access Ročník 11; s. 79750 - 79776
Hlavní autoři:	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:	angličtina
Vydáno:	Piscataway IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!