A Feature Selection Method of Parallel Grey Wolf Optimization Algorithm Based on Spark

2019 ◽  
Author(s):  
Hongwei Chen ◽  
Lin Han ◽  
Zhou Hu ◽  
Qiao Hou ◽  
Zhiwei Ye ◽  
...  
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Feature Selection Method ◽  
Selection Method ◽  
Grey Wolf ◽  
Grey Wolf Optimization

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.

Feature Selection Method based on Grey Wolf Optimization and Simulated Annealing

2019 ◽  
Vol 12 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Feature Selection ◽  
Simulated Annealing ◽  
Feature Selection Method ◽  
Optimization Method ◽  
Selection Method ◽  
Attribute Selection ◽  
Binary Particle Swarm Optimization ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Whale Optimization

Feature selection sometimes also known as attribute subset selection is a process in which optimal subset of features are elected with respect to target data by reducing dimensionality and removing irrelevant data. There will be 2^n possible solutions for a dataset having n number of features which is difficult to solve by conventional attribute selection method. In such cases metaheuristic-based methods generally outruns the conventional methods. Therefore, this paper introduces a binary metaheuristic feature selection method bGWOSA which is based on grey wolf optimization and simulated annealing. The proposed feature selection method uses simulated annealing for enhancing the exploitation rate of grey wolf optimization method. The performance of the proposed binary feature selection method has been examined on the ten feature selection benchmark datasets taken from UCI repository and compared with binary cuckoo search, binary particle swarm optimization, binary grey wolf optimization, binary bat algorithm and binary hybrid whale optimization method. Statistical analysis and Experimental results validate the efficacy of proposed method.

Sign in / Sign up

Export Citation Format

Share Document