[HTML][HTML] An external attention-based feature ranker for large-scale feature selection
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 …
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
As a key preprocessing step in classification, feature selection involves two conflicting
objectives: maximizing the classification accuracy and minimizing the number of selected …
objectives: maximizing the classification accuracy and minimizing the number of selected …
Information gain ratio-based subfeature grou** empowers particle swarm optimization for feature selection
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 …
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
The high-dimensional datasets in various domains, such as text categorization, information
retrieval and bioinformatics, have highlighted the importance of feature selection in data …
retrieval and bioinformatics, have highlighted the importance of feature selection in data …
Feature clustering-Assisted feature selection with differential evolution
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 …
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
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 …
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 …
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
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 …
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 …
needs to consider both minimizing the size of feature subset and maximizing the …
Reinforcement learning-based multi-objective differential evolution algorithm for feature selection
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 …
dataset by reducing dimensionality and improving accuracy. In this study, a reinforcement …