A comprehensive survey on recent metaheuristics for feature selection
Feature selection has become an indispensable machine learning process for data
preprocessing due to the ever-increasing sizes in actual data. There have been many …
preprocessing due to the ever-increasing sizes in actual data. There have been many …
[HTML][HTML] Differential evolution: A recent review based on state-of-the-art works
Differential evolution (DE) is a popular evolutionary algorithm inspired by Darwin's theory of
evolution and has been studied extensively to solve different areas of optimisation and …
evolution and has been studied extensively to solve different areas of optimisation and …
Review of swarm intelligence-based feature selection methods
In the past decades, the rapid growth of computer and database technologies has led to the
rapid growth of large-scale datasets. On the other hand, data mining applications with high …
rapid growth of large-scale datasets. On the other hand, data mining applications with high …
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) …
A survey on evolutionary multiobjective feature selection in classification: approaches, applications, and challenges
Maximizing the classification accuracy and minimizing the number of selected features are
two primary objectives in feature selection, which is inherently a multiobjective task …
two primary objectives in feature selection, which is inherently a multiobjective task …
An enhanced black widow optimization algorithm for feature selection
G Hu, B Du, X Wang, G Wei - Knowledge-Based Systems, 2022 - Elsevier
Feature selection is an important data processing method to reduce dimension of the raw
datasets while preserving the information as much as possible. In this paper, an enhanced …
datasets while preserving the information as much as possible. In this paper, an enhanced …
Multiobjective particle swarm optimization for feature selection with fuzzy cost
Y Hu, Y Zhang, D Gong - IEEE Transactions on Cybernetics, 2020 - ieeexplore.ieee.org
Feature selection (FS) is an important data processing technique in the field of machine
learning. There have been various FS methods, but all assume that the cost associated with …
learning. There have been various FS methods, but all assume that the cost associated with …
DMDE: Diversity-maintained multi-trial vector differential evolution algorithm for non-decomposition large-scale global optimization
DE algorithms have outstanding performance in solving complex problems. However, they
also have highlighted the need for an effective approach to alleviating the risk of premature …
also have highlighted the need for an effective approach to alleviating the risk of premature …
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 …