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
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
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 …