A random walk Grey wolf optimizer based on dispersion factor for feature selection on chronic disease prediction
K Deep - Expert Systems with Applications, 2022 - Elsevier
In the field of Chronic disease prediction, identifying the relevant features plays an important
role for early disease diagnosis. With a high dimensionality of data, search for an adequate …
role for early disease diagnosis. With a high dimensionality of data, search for an adequate …
An efficient high-dimensional feature selection approach driven by enhanced multi-strategy grey wolf optimizer for biological data classification
Biological data generally contain complex and high-dimensional samples. In addition, the
number of samples in biological datasets is much fewer than the number of features, so the …
number of samples in biological datasets is much fewer than the number of features, so the …
A review of grey wolf optimizer-based feature selection methods for classification
Feature selection is imperative in machine learning and data mining when we have high-
dimensional datasets with redundant, nosy and irrelevant features. The area of feature …
dimensional datasets with redundant, nosy and irrelevant features. The area of feature …
Early prediction of chronic disease using an efficient machine learning algorithm through adaptive probabilistic divergence based feature selection approach
S Hegde, MR Mundada - International Journal of Pervasive …, 2021 - emerald.com
Purpose According to the World Health Organization, by 2025, the contribution of chronic
disease is expected to rise by 73% compared to all deaths and it is considered as global …
disease is expected to rise by 73% compared to all deaths and it is considered as global …
A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection
M Abdel-Basset, D El-Shahat, I El-Henawy… - Expert Systems with …, 2020 - Elsevier
Because of their high dimensionality, dealing with large datasets can hinder the data mining
process. Thus, the feature selection is a pre-process mandatory phase for reducing the …
process. Thus, the feature selection is a pre-process mandatory phase for reducing the …
A new hybrid algorithm based on grey wolf optimization and crow search algorithm for unconstrained function optimization and feature selection
Grey wolf optimizer (GWO) is a very efficient metaheuristic inspired by the hierarchy of the
Canis lupus wolves. It has been extensively employed to a variety of practical applications …
Canis lupus wolves. It has been extensively employed to a variety of practical applications …
Feature selection method based on grey wolf optimization for coronary artery disease classification
Cardiovascular disease has been declared as one of the deadly illness that affects humans
in the Middle and Old ages across the globe. One of the cardiovascular disease known as …
in the Middle and Old ages across the globe. One of the cardiovascular disease known as …
Binary grey wolf optimizer with mutation and adaptive k-nearest neighbour for feature selection in Parkinson's disease diagnosis
RR Rajammal, S Mirjalili, G Ekambaram… - Knowledge-Based …, 2022 - Elsevier
Disease identification and classification relies on Feature Selection (FS) to find the relevant
features for accurate medical diagnosis. FS is an optimization problem solved with the help …
features for accurate medical diagnosis. FS is an optimization problem solved with the help …
Improved binary grey wolf optimizer and its application for feature selection
Abstract Grey Wolf Optimizer (GWO) is a new swarm intelligence algorithm mimicking the
behaviours of grey wolves. Its abilities include fast convergence, simplicity and easy …
behaviours of grey wolves. Its abilities include fast convergence, simplicity and easy …
[HTML][HTML] Binary starling murmuration optimizer algorithm to select effective features from medical data
Feature selection is an NP-hard problem to remove irrelevant and redundant features with
no predictive information to increase the performance of machine learning algorithms. Many …
no predictive information to increase the performance of machine learning algorithms. Many …