Open Access

ARTICLE

BHGSO: Binary Hunger Games Search Optimization Algorithm for Feature Selection Problem

R. Manjula Devi¹, M. Premkumar², Pradeep Jangir³, B. Santhosh Kumar⁴, Dalal Alrowaili⁵, Kottakkaran Sooppy Nisar^6,*

1 Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, 638060, Tamil Nadu, India
2 Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bengaluru, 560078, Karnataka, India
3 Rajasthan Rajya Vidyut Prasaran Nigam, Sikar, 332025, Rajasthan, India
4 Department of Computer Science and Engineering, Guru Nanak Institute of Technology, Hyderabad, 501506, Telangana, India
5 Mathematics Department, College of Science, Jouf University, Sakaka, P.O. Box: 2014, Saudi Arabia
6 Department of Mathematics, College of Arts and Sciences, Prince Sattam bin Abdulaziz University, Wadi Aldawaser, 11991, Saudi Arabia

* Corresponding Author: Kottakkaran Sooppy Nisar. Email:

Computers, Materials & Continua 2022, 70(1), 557-579. https://doi.org/10.32604/cmc.2022.019611

Received 18 April 2021; Accepted 19 May 2021; Issue published 07 September 2021

Abstract

In machine learning and data mining, feature selection (FS) is a traditional and complicated optimization problem. Since the run time increases exponentially, FS is treated as an NP-hard problem. The researcher’s effort to build a new FS solution was inspired by the ongoing need for an efficient FS framework and the success rates of swarming outcomes in different optimization scenarios. This paper presents two binary variants of a Hunger Games Search Optimization (HGSO) algorithm based on V- and S-shaped transfer functions within a wrapper FS model for choosing the best features from a large dataset. The proposed technique transforms the continuous HGSO into a binary variant using V- and S-shaped transfer functions (BHGSO-V and BHGSO-S). To validate the accuracy, 16 famous UCI datasets are considered and compared with different state-of-the-art metaheuristic binary algorithms. The findings demonstrate that BHGSO-V achieves better performance in terms of the selected number of features, classification accuracy, run time, and fitness values than other state-of-the-art algorithms. The results demonstrate that the BHGSO-V algorithm can reduce dimensionality and choose the most helpful features for classification problems. The proposed BHGSO-V achieves 95% average classification accuracy for most of the datasets, and run time is less than 5 sec. for low and medium dimensional datasets and less than 10 sec for high dimensional datasets.

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Citations