A Novel Wrapper-Based Feature Selection for Heart Failure Prediction Using an Adaptive Particle Swarm Grey Wolf Optimization

2021 ◽  
pp. 315-336
Author(s):  
Tuan Minh Le ◽  
Tan Nhat Pham ◽  
Son Vu Truong Dao
Keyword(s):  
Heart Failure ◽  
Feature Selection ◽  
Particle Swarm ◽  
Failure Prediction ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Selection For

Feature selection for image steganalysis using levy flight-based grey wolf optimization

2018 ◽  
Vol 78 (2) ◽  
pp. 1473-1494 ◽  
Author(s):  
Yadunath Pathak ◽  
K. V. Arya ◽  
Shailendra Tiwari
Keyword(s):  
Feature Selection ◽  
Lévy Flight ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Image Steganalysis ◽  
Levy Flight ◽  
Selection For

scholarly journals Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1816
Author(s):  
Hailun Xie ◽  
Li Zhang ◽  
Chee Peng Lim ◽  
Yonghong Yu ◽  
Han Liu
Keyword(s):  
Feature Selection ◽  
Particle Swarm ◽  
Nonlinear Function ◽  
Particle Swarm Optimisation ◽  
Data Sets ◽  
Discriminative Feature ◽  
Advanced Search ◽  
Selection For ◽  
Selection Tasks ◽  
New Strategies

In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets.

scholarly journals Onboard Cloud-enabled Parkinson Disease Identification System Using Enhanced Grey Wolf Optimization

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 1360-1372
Author(s):  
Ramaprabha Jayaram ◽  
T. Senthil Kumar
Keyword(s):  
Feature Selection ◽  
Parkinson Disease ◽  
Neurodegenerative Disorder ◽  
Feature Selection Method ◽  
Selection Method ◽  
Identification System ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Time Duration ◽  
Disease Identification

Parkinson disease is a rigorous neurodegenerative disorder characterized by the cognitive behavior ending with disability problems. Especially, the elderly people should be given more care and spend more time duration to diagnose when they are at risk. It is more important to identify and diagnose Parkinson disease at an earlier stage rather than spending too much of cost later stages. Different ways of diagnosing the disease ranging from gene analysis to gait behavior, speech, writing test and olfactory models were used in the conventional testing process. In order to increase the patient’s quality of life and minimize the cost of healthcare utilization, an Onboard Cloud-Enabled Parkinson Disease Identification System (OCPDIS) is proposed. An enhanced grey wolf optimization is explored along with the differential evolution techniques to form an effective hybrid feature selection method. Using this feature selection method in the enhanced k-Nearest Neighbor (k-NN) classifier model could substantially improve the prediction time and prediction accuracy.

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