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A review of feature selection methods in medical applications
Feature selection is a preprocessing technique that identifies the key features of a given
problem. It has traditionally been applied in a wide range of problems that include biological …
problem. It has traditionally been applied in a wide range of problems that include biological …
Feature selection and its use in big data: challenges, methods, and trends
Feature selection has been an important research area in data mining, which chooses a
subset of relevant features for use in the model building. This paper aims to provide an …
subset of relevant features for use in the model building. This paper aims to provide an …
A high-dimensional feature selection method based on modified Gray Wolf Optimization
H Pan, S Chen, H **ong - Applied Soft Computing, 2023 - Elsevier
For data mining tasks on high-dimensional data, feature selection is a necessary pre-
processing stage that plays an important role in removing redundant or irrelevant features …
processing stage that plays an important role in removing redundant or irrelevant features …
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) …
Feature selection using neighborhood entropy-based uncertainty measures for gene expression data classification
Gene expression data classification is an important technology for cancer diagnosis in
bioinformatics and has been widely researched. Due to the large number of genes and the …
bioinformatics and has been widely researched. Due to the large number of genes and the …
Copula entropy-based golden jackal optimization algorithm for high-dimensional feature selection problems
Feature selection (FS) is a crucial process that aims to remove unnecessary features from
datasets. It plays a role in data mining and machine learning (ML) by reducing the risk …
datasets. It plays a role in data mining and machine learning (ML) by reducing the risk …
A novel hybrid Harris hawks optimization and support vector machines for drug design and discovery
Cheminformatics has main research factors due to increasing size of the search space of
chemical compound databases and the importance of similarity measurements for drug …
chemical compound databases and the importance of similarity measurements for drug …
Feature selection using Information Gain and decision information in neighborhood decision system
K Qu, J Xu, Q Hou, K Qu, Y Sun - Applied Soft Computing, 2023 - Elsevier
Feature selection is a significant preprocessing technique for data mining, which can
promote the accuracy of data classification and shrink feature space by eliminating …
promote the accuracy of data classification and shrink feature space by eliminating …
A steering-matrix-based multiobjective evolutionary algorithm for high-dimensional feature selection
In recent years, multiobjective evolutionary algorithms (MOEAs) have been demonstrated to
show promising performance in feature selection (FS) tasks. However, designing an MOEA …
show promising performance in feature selection (FS) tasks. However, designing an MOEA …
[HTML][HTML] An adaptive inertia weight teaching-learning-based optimization algorithm and its applications
This paper presents an effective metaheuristic algorithm called teaching learning-based
optimization which is widely applied to solve the various real-world optimization problems …
optimization which is widely applied to solve the various real-world optimization problems …