Binary grey wolf optimization approaches for feature selection

By Eid Emary, Hossam M. Zawbaa, Aboul Ella Otifey Hassanien. Neurocomputing, 2016

1,286 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/1913f3fd280371d4eb54be88e32bd3c99cfc7d77

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Citations in the corpus

An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems (2018, methodology): “I For this purpose, the binary bGWO [74], BGSA [70], and BBA [75] algorithms are considered here to verify the performance of the proposed BSSA_S3_CP technique.”
A novel Random Walk Grey Wolf Optimizer (2019, neutral): “[22] proposed the binary version of GWO for feature selection.”
Binary ant lion approaches for feature selection (2016, methodology): “Lots of research used a binary version of bio-inspired algorithms [22], [23], [24], [25].”
Using a kernel extreme learning machine with grey wolf optimization to predict the displacement of step-like landslide (2019, background): “Landslide displacement is the result of the combined effects of internal geological conditions and external triggering factors and, consequently, there is a large element of uncertainty (Federico et al. 2012;Miao et al. 2017).”
Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection (2019, methodology): “According to [11] the updating mechanism of wolves is a function of three vectors position namely x 1 , x 2 ; x 3 which promotes every wolf to the ﬁrst three best solutions.”
Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems (2022, background): “These application domains can be categorized into: machine learning (Emary et al., 2016, 2015; Al-Tashi et al., 2019), engineering (Kohli and Arora, 2018; Yıldız and Yıldız, 2018), wireless sensor network (Kaushik et al., 2019; Rajakumar et al., 2017), environmental modeling (Tikhamarine et al.,…”
Grey wolf optimizer: a review of recent variants and applications (2017, background): “Their fitness function considers the accuracy rate along with the number of selected features as in [22].”
Swarm Intelligence Algorithms for Feature Selection: A Review (2018, methodology): “Datasets from 73 up to 5000 features are used in [34].”