H
Hossam M. Zawbaa
Researcher at Beni-Suef University
Publications - 68
Citations - 3687
Hossam M. Zawbaa is an academic researcher from Beni-Suef University. The author has contributed to research in topics: Feature selection & Particle swarm optimization. The author has an hindex of 28, co-authored 65 publications receiving 2514 citations. Previous affiliations of Hossam M. Zawbaa include Babeș-Bolyai University & Arab Open University.
Papers
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Journal ArticleDOI
Binary grey wolf optimization approaches for feature selection
TL;DR: Results prove the capability of the proposed binary version of grey wolf optimization (bGWO) to search the feature space for optimal feature combinations regardless of the initialization and the used stochastic operators.
Journal ArticleDOI
Binary ant lion approaches for feature selection
TL;DR: Binary variants of the ant lion optimizer (ALO) are proposed and used to select the optimal feature subset for classification purposes in wrapper-mode and prove the capability of the proposed binary algorithms to search the feature space for optimal feature combinations regardless of the initialization and the used stochastic operators.
Book ChapterDOI
Feature Subset Selection Approach by Gray-Wolf Optimization
TL;DR: A classification accuracy-based fitness function is proposed by gray-wolf optimizer to find optimal feature subset and proves much robustness against initialization in comparison with PSO and GA optimizers.
Journal ArticleDOI
Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks
TL;DR: A variant of gray wolf optimization (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed and its performance is assessed on solving feature selection problems and on finding optimal weights for neural networks algorithm.
Journal ArticleDOI
Feature Selection via Chaotic Antlion Optimization.
TL;DR: An optimization approach for the feature selection problem that considers a “chaotic” version of the antlion optimizer method, a nature-inspired algorithm that mimics the hunting mechanism of antlions in nature to improve the tradeoff between exploration and exploitation.