A review of the modification strategies of the nature inspired algorithms for feature selection problem
This survey is an effort to provide a research repository and a useful reference for
researchers to guide them when planning to develop new Nature-inspired Algorithms …
researchers to guide them when planning to develop new Nature-inspired Algorithms …
A survey on swarm intelligence approaches to feature selection in data mining
One of the major problems in Big Data is a large number of features or dimensions, which
causes the issue of “the curse of dimensionality” when applying machine learning …
causes the issue of “the curse of dimensionality” when applying machine learning …
A survey on evolutionary computation approaches to feature selection
Feature selection is an important task in data mining and machine learning to reduce the
dimensionality of the data and increase the performance of an algorithm, such as a …
dimensionality of the data and increase the performance of an algorithm, such as a …
A new representation in PSO for discretization-based feature selection
In machine learning, discretization and feature selection (FS) are important techniques for
preprocessing data to improve the performance of an algorithm on high-dimensional data …
preprocessing data to improve the performance of an algorithm on high-dimensional data …
Survey on data science with population-based algorithms
This paper discusses the relationship between data science and population-based
algorithms, which include swarm intelligence and evolutionary algorithms. We reviewed two …
algorithms, which include swarm intelligence and evolutionary algorithms. We reviewed two …
Gene selection from microarray gene expression data for classification of cancer subgroups employing PSO and adaptive K-nearest neighborhood technique
These days, microarray gene expression data are playing an essential role in cancer
classifications. However, due to the availability of small number of effective samples …
classifications. However, due to the availability of small number of effective samples …
A new binary particle swarm optimization approach: Momentum and dynamic balance between exploration and exploitation
Particle swarm optimization (PSO) is a heuristic optimization algorithm generally applied to
continuous domains. Binary PSO is a form of PSO applied to binary domains but uses the …
continuous domains. Binary PSO is a form of PSO applied to binary domains but uses the …
[HTML][HTML] A hybrid gene selection algorithm for microarray cancer classification using genetic algorithm and learning automata
H Motieghader, A Najafi, B Sadeghi… - Informatics in Medicine …, 2017 - Elsevier
Cancer classification is an important problem in cancer diagnosis and treatment. One of the
most effective methods in cancer classification is gene selection. However, selecting a …
most effective methods in cancer classification is gene selection. However, selecting a …
A novel gene selection algorithm for cancer classification using microarray datasets
R Alanni, J Hou, H Azzawi, Y Xiang - BMC medical genomics, 2019 - Springer
Background Microarray datasets are an important medical diagnostic tool as they represent
the states of a cell at the molecular level. Available microarray datasets for classifying cancer …
the states of a cell at the molecular level. Available microarray datasets for classifying cancer …
Efficient high-dimension feature selection based on enhanced equilibrium optimizer
S Ouadfel, M Abd Elaziz - Expert Systems with Applications, 2022 - Elsevier
Feature selection (FS) is an important task in any classification process and aims to choose
the smallest features number that yields higher classification accuracy. FS can be formulated …
the smallest features number that yields higher classification accuracy. FS can be formulated …