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 …
Advances in nature-inspired metaheuristic optimization for feature selection problem: A comprehensive survey
The main objective of feature selection is to improve learning performance by selecting
concise and informative feature subsets, which presents a challenging task for machine …
concise and informative feature subsets, which presents a challenging task for machine …
A textual-based featuring approach for depression detection using machine learning classifiers and social media texts
Depression is one of the leading causes of suicide worldwide. However, a large percentage
of cases of depression go undiagnosed and, thus, untreated. Previous studies have found …
of cases of depression go undiagnosed and, thus, untreated. Previous studies have found …
Boosted sooty tern optimization algorithm for global optimization and feature selection
Feature selection (FS) represents an optimization problem that aims to simplify and improve
the quality of highly dimensional datasets through selecting prominent features and …
the quality of highly dimensional datasets through selecting prominent features and …
SemiACO: A semi-supervised feature selection based on ant colony optimization
Feature selection is one of the most efficient procedures for reducing the dimensionality of
high-dimensional data by choosing a practical subset of features. Since labeled samples are …
high-dimensional data by choosing a practical subset of features. Since labeled samples are …
Feature selection methods for text classification: a systematic literature review
JT Pintas, LAF Fernandes, ACB Garcia - Artificial Intelligence Review, 2021 - Springer
Feature Selection (FS) methods alleviate key problems in classification procedures as they
are used to improve classification accuracy, reduce data dimensionality, and remove …
are used to improve classification accuracy, reduce data dimensionality, and remove …
COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions
Classification of COVID-19 X-ray images to determine the patient's health condition is a
critical issue these days since X-ray images provide more information about the patient's …
critical issue these days since X-ray images provide more information about the patient's …
A review of semi-supervised learning for text classification
JM Duarte, L Berton - Artificial Intelligence Review, 2023 - Springer
A huge amount of data is generated daily leading to big data challenges. One of them is
related to text mining, especially text classification. To perform this task we usually need a …
related to text mining, especially text classification. To perform this task we usually need a …
Gene selection and classification of microarray data method based on mutual information and moth flame algorithm.
Several techniques or methods may help in detecting diseases and cancer. Creating an
effective method for extracting disease information is one of the major challenges in the …
effective method for extracting disease information is one of the major challenges in the …
An improved gorilla troops optimizer for global optimization problems and feature selection
Abstract The Artificial Gorilla Groups Optimizer (GTO) is a novel metaheuristic algorithm that
takes its cues from the collective intelligence of wild gorilla troops. Although it has shown …
takes its cues from the collective intelligence of wild gorilla troops. Although it has shown …