[HTML][HTML] An external attention-based feature ranker for large-scale feature selection

Y Xue, C Zhang, F Neri, M Gabbouj, Y Zhang - Knowledge-Based Systems, 2023 - Elsevier
An important problem in data science, feature selection (FS) consists of finding the optimal
subset of features and eliminating irrelevant or redundant features. The FS task on high …

A bidirectional dynamic grou** multi-objective evolutionary algorithm for feature selection on high-dimensional classification

K Yu, S Sun, J Liang, K Chen, B Qu, C Yue, L Wang - Information Sciences, 2023 - Elsevier
As a key preprocessing step in classification, feature selection involves two conflicting
objectives: maximizing the classification accuracy and minimizing the number of selected …

Information gain ratio-based subfeature grou** empowers particle swarm optimization for feature selection

J Gao, Z Wang, T **, J Cheng, Z Lei, S Gao - Knowledge-Based Systems, 2024 - Elsevier
Feature selection is a critical preprocessing step in machine learning with significant real-
world applications. Despite the widespread use of particle swarm optimization (PSO) for …

[HTML][HTML] An evolutionary filter approach to feature selection in classification for both single-and multi-objective scenarios

E Hancer, B Xue, M Zhang - Knowledge-Based Systems, 2023 - Elsevier
The high-dimensional datasets in various domains, such as text categorization, information
retrieval and bioinformatics, have highlighted the importance of feature selection in data …

Feature clustering-Assisted feature selection with differential evolution

P Wang, B Xue, J Liang, M Zhang - Pattern Recognition, 2023 - Elsevier
Modern data collection technologies may produce thousands of or even more features in a
single dataset. The high dimensionality of data poses a barrier to determining discriminating …

An accurate metaheuristic mountain gazelle optimizer for parameter estimation of single-and double-diode photovoltaic cell models

R Abbassi, S Saidi, S Urooj, BN Alhasnawi, MA Alawad… - Mathematics, 2023 - mdpi.com
Accurate parameter estimation is crucial and challenging for the design and modeling of PV
cells/modules. However, the high degree of non-linearity of the typical I–V characteristic …

Multi-objective optimization algorithm based on clustering guided binary equilibrium optimizer and NSGA-III to solve high-dimensional feature selection problem

M Zhang, JS Wang, Y Liu, HM Song, JN Hou… - Information …, 2023 - Elsevier
Feature selection (FS) is an indispensable activity in machine learning, whose purpose is to
identify relevant predictive values from a high-dimensional feature space to improve …

[HTML][HTML] Improved binary differential evolution with dimensionality reduction mechanism and binary stochastic search for feature selection

B Ahadzadeh, M Abdar, F Safara, L Aghaei… - Applied Soft …, 2024 - Elsevier
Computer systems store massive amounts of data with numerous features, leading to the
need to extract the most important features for better classification in a wide variety of …

MPEA-FS: A decomposition-based multi-population evolutionary algorithm for high-dimensional feature selection

W Li, Z Chai - Expert Systems with Applications, 2024 - Elsevier
The challenge of high-dimensional feature selection (FS) lies in the search technique, which
needs to consider both minimizing the size of feature subset and maximizing the …

Reinforcement learning-based multi-objective differential evolution algorithm for feature selection

X Yu, Z Hu, W Luo, Y Xue - Information Sciences, 2024 - Elsevier
Feature Selection (FS) can be used to determine the optimal subset of features from a raw
dataset by reducing dimensionality and improving accuracy. In this study, a reinforcement …