Binary ant lion approaches for feature selection
By Eid Emary, Hossam M. Zawbaa, Aboul Ella Otifey Hassanien. Neurocomputing, 2016
436 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/1fe6676b10d721105b8ae3b6ded9f70483113c80
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Tansel Dökeroğlu
- Ali Asghar Heidari
- Ayça Deniz
- Hakan Ezgi Kiziloz
- Ibrahim Aljarah
- Hossam Faris
- Majdi M. Mafarja
- Seyedali Mirjalili
In Computational Theory and Mathematics
- Tansel Dökeroğlu
- Ali Asghar Heidari
- Ayça Deniz
- Hakan Ezgi Kiziloz
- Hossam Faris
- Ibrahim Aljarah
- Majdi M. Mafarja
- Ala’ M. Al-Zoubi
Citations in the corpus
- An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems (2018, methodology): “AC CE PT ED M AN US CR IP T the original dataset, and α ∈ [0, 1], β = (1 − α) are two parameters corresponding to the importance of classification quality and subset length as per recommendations in [41].”
- A comprehensive survey on recent metaheuristics for feature selection (2022, methodology): “developed ALO algorithms for wrapper-based feature subset selection task in classification [11].”