Open Access

ARTICLE

Feature Selection Using Grey Wolf Optimization with Random Differential Grouping

by R. S. Latha^1,*, B. Saravana Balaji², Nebojsa Bacanin³, Ivana Strumberger³, Miodrag Zivkovic³, Milos Kabiljo³

1 Department of Computer Science and Engineering, Kongu Engineering College, Erode, 638060, Tamilnadu, India
2 Department of Information Technology, Lebanese French University, Erbil, Iraq
3 Singidunum University, Belgrade, 160622, Serbia

* Corresponding Author: R. S. Latha. Email:

Computer Systems Science and Engineering 2022, 43(1), 317-332. https://doi.org/10.32604/csse.2022.020487

Received 26 May 2021; Accepted 05 August 2021; Issue published 23 March 2022

Abstract

Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage capacity. The user’s access over the internet creates massive data processing over the internet. Big data require an intelligent feature selection model by addressing huge varieties of data. Traditional feature selection techniques are only applicable to simple data mining. Intelligent techniques are needed in big data processing and machine learning for an efficient classification. Major feature selection algorithms read the input features as they are. Then, the features are preprocessed and classified. Here, an algorithm does not consider the relatedness. During feature selection, all features are misread as outputs. Accordingly, a less optimal solution is achieved. In our proposed research, we focus on the feature selection by using supervised learning techniques called grey wolf optimization (GWO) with decomposed random differential grouping (DrnDG-GWO). First, decomposition of features into subsets based on relatedness in variables is performed. Random differential grouping is performed using a fitness value of two variables. Now, every subset is regarded as a population in GWO techniques. The combination of supervised machine learning with swarm intelligence techniques produces best feature optimization results in this research. Once the features are optimized, we classify using advanced kNN process for accurate data classification. The result of DrnDG-GWO is compared with those of the standard GWO and GWO with PSO for feature selection to compare the efficiency of the proposed algorithm. The accuracy and time complexity of the proposed algorithm are 98% and 5 s, which are better than the existing techniques.

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Keywords

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