Swarm Intelligence-Based Feature Selection: An Improved Binary Grey Wolf Optimization Method

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.

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

Webology ◽

10.14704/web/v18si04/web18203 ◽

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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Improved Local Search Based Grey Wolf Optimization for Feature Selection

Lecture Notes on Multidisciplinary Industrial Engineering - Proceedings of the International Conference on Industrial and Manufacturing Systems (CIMS-2020) ◽

10.1007/978-3-030-73495-4_26 ◽

2021 ◽

pp. 371-387

Author(s):

Rahul Hans ◽

Harjot Kaur

Keyword(s):

Feature Selection ◽

Local Search ◽

Grey Wolf ◽

Grey Wolf Optimization

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Planning for Distribution System with Grey Wolf Optimization Method

Journal of Electrical Engineering and Technology ◽

10.1007/s42835-020-00419-4 ◽

2020 ◽

Vol 15 (4) ◽

pp. 1485-1499

Author(s):

Muhammad Mohsin Ansari ◽

Chuangxin Guo ◽

Muhammad Suhail Shaikh ◽

Nitish Chopra ◽

Inzamamul Haq ◽

...

Keyword(s):

Distribution System ◽

Optimization Method ◽

Grey Wolf ◽

Grey Wolf Optimization

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Application of a Supervised Learning Machine for Accurate Prognostication of Hydrogen Contents of Bio-Oil

International Journal of Chemical Engineering ◽

10.1155/2021/7548251 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Binghui Xu ◽

Tzu-Chia Chen ◽

Danial Ahangari ◽

S. M. Alizadeh ◽

Marischa Elveny ◽

...

Keyword(s):

High Performance ◽

Laboratory Data ◽

Optimization Method ◽

Good Alternative ◽

Support Vector ◽

Support Vector Machine Algorithm ◽

Grey Wolf ◽

Grey Wolf Optimization ◽

Bio Oil ◽

Learning Machine

This paper deals with modeling hydrogen contents of bio-oil (H-BO) as a function of pyrolysis conditions and biomass compositions of feedstock. The support vector machine algorithm optimized by the grey wolf optimization method has been used in modeling this end. Comprehensive data for this purpose were aggregated from previous sources and reports. The results of various analyses showed that this algorithm has a high ability to predict actual results. The calculated values of R2, MRE (%), MSE, and RMSE were obtained as 0.973, 1.98, 0.0568, and 0.241, respectively. According to the results of various analyses, the high performance of this model in predicting the output values was proved. Also, by comparing this model with the previously proposed models in terms of accuracy, it was observed that this model had a better performance. This algorithm can be a good alternative to costly and time-consuming laboratory data.

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