A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities
Specialized data preparation techniques, ranging from data cleaning, outlier detection,
missing value imputation, feature selection (FS), amongst others, are procedures required to …
missing value imputation, feature selection (FS), amongst others, are procedures required to …
A review of population-based metaheuristics for large-scale black-box global optimization—Part II
This article is the second part of a two-part survey series on large-scale global optimization.
The first part covered two major algorithmic approaches to large-scale optimization, namely …
The first part covered two major algorithmic approaches to large-scale optimization, namely …
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 multi-objective evolutionary algorithm with interval based initialization and self-adaptive crossover operator for large-scale feature selection in classification
Feature selection (FS) is an important data pre-processing technique in classification. In
most cases, FS can improve classification accuracy and reduce feature dimension, so it can …
most cases, FS can improve classification accuracy and reduce feature dimension, so it can …
[HTML][HTML] Brain tumor detection in MR image using superpixels, principal component analysis and template based K-means clustering algorithm
In the present era, human brain tumor is the extremist dangerous and devil to the human
being that leads to certain death. Furthermore, the brain tumor arises more complexity of …
being that leads to certain death. Furthermore, the brain tumor arises more complexity of …
A hyper learning binary dragonfly algorithm for feature selection: A COVID-19 case study
The rapid expansion of information science has caused the issue of “the curse of
dimensionality”, which will negatively affect the performance of the machine learning model …
dimensionality”, which will negatively affect the performance of the machine learning model …
Multi-objective feature selection with missing data in classification
Y Xue, Y Tang, X Xu, J Liang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Feature selection (FS) is an important research topic in machine learning. Usually, FS is
modelled as a bi-objective optimization problem whose objectives are: 1) classification …
modelled as a bi-objective optimization problem whose objectives are: 1) classification …
A novel community detection based genetic algorithm for feature selection
The feature selection is an essential data preprocessing stage in data mining. The core
principle of feature selection seems to be to pick a subset of possible features by excluding …
principle of feature selection seems to be to pick a subset of possible features by excluding …
A survey on binary metaheuristic algorithms and their engineering applications
This article presents a comprehensively state-of-the-art investigation of the engineering
applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based …
applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based …