A review of the modification strategies of the nature inspired algorithms for feature selection problem
This survey is an effort to provide a research repository and a useful reference for
researchers to guide them when planning to develop new Nature-inspired Algorithms …
researchers to guide them when planning to develop new Nature-inspired Algorithms …
An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future …
Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early
adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior …
adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior …
Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study
The whale optimization algorithm (WOA) is a prominent problem solver which is broadly
applied to solve NP-hard problems such as feature selection. However, it and most of its …
applied to solve NP-hard problems such as feature selection. However, it and most of its …
A fast hybrid feature selection based on correlation-guided clustering and particle swarm optimization for high-dimensional data
XF Song, Y Zhang, DW Gong… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The “curse of dimensionality” and the high computational cost have still limited the
application of the evolutionary algorithm in high-dimensional feature selection (FS) …
application of the evolutionary algorithm in high-dimensional feature selection (FS) …
Feature selection using bare-bones particle swarm optimization with mutual information
X Song, Y Zhang, D Gong, X Sun - Pattern Recognition, 2021 - Elsevier
Feature selection (FS) is an important data processing method in pattern recognition and
data mining. Due to not considering characteristics of the FS problem itself, traditional …
data mining. Due to not considering characteristics of the FS problem itself, traditional …
Binary optimization using hybrid grey wolf optimization for feature selection
A binary version of the hybrid grey wolf optimization (GWO) and particle swarm optimization
(PSO) is proposed to solve feature selection problems in this paper. The original PSOGWO …
(PSO) is proposed to solve feature selection problems in this paper. The original PSOGWO …
Adaptive crossover operator based multi-objective binary genetic algorithm for feature selection in classification
Feature selection is a key pre-processing technique for classification which aims at
removing irrelevant or redundant features from a given dataset. Generally speaking, feature …
removing irrelevant or redundant features from a given dataset. Generally speaking, feature …
Hybrid whale optimization algorithm with simulated annealing for feature selection
MM Mafarja, S Mirjalili - Neurocomputing, 2017 - Elsevier
Hybrid metaheuristics are of the most interesting recent trends in optimization and memetic
algorithms. In this paper, two hybridization models are used to design different feature …
algorithms. In this paper, two hybridization models are used to design different feature …
Variable-size cooperative coevolutionary particle swarm optimization for feature selection on high-dimensional data
XF Song, Y Zhang, YN Guo, XY Sun… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Evolutionary feature selection (FS) methods face the challenge of “curse of dimensionality”
when dealing with high-dimensional data. Focusing on this challenge, this article studies a …
when dealing with high-dimensional data. Focusing on this challenge, this article studies a …
Dynamic salp swarm algorithm for feature selection
Recently, many optimization algorithms have been applied for Feature selection (FS)
problems and show a clear outperformance in comparison with traditional FS methods …
problems and show a clear outperformance in comparison with traditional FS methods …