Vol.13, No.4, November 2024.                                                                                                                                                                          ISSN: 2217-8309

                                                                                                                                                                                                                       eISSN: 2217-8333

TEM Journal

TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS

Association for Information Communication Technology Education and Science

An Effective Hybrid Feature Selection Method Based on an Improved Artificial GTO Algorithm for Medical Datasets

Abd Al-Baset Rashed Saabia, Mondher Frikha

© 2024 Abd Al-Baset Rashed Saabia, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

Citation Information: TEM Journal. Volume 13, Issue 4, Pages 2715-2723, ISSN 2217-8309, DOI: 10.18421/TEM134-09, November 2024.

Received: 28 April 2024.

Revised: 29 August 2024.
Accepted: 14 October 2024.
Published: 27 November 2024.

Abstract:

Feature subset selection is considered as the most essential pre-processing step. Metaheuristic approaches may be employed to discover a solution to difficulties in feature selection, which can be viewed as an optimisation problem. The aim of the system is to provide a hybrid binary metaheuristic algorithm that combines gorilla troop optimisation and genetic algorithm to handle the feature selection issue effectively. This new method is called GTO-GA. To ensure that the optimisation technique converges fast and properly and to enhance the exploration process, the GA were used. The suggested technique is tested for stability and robustness using 16 medical datasets taken from the Kaggle and UCI repositories. To evaluate the chosen features’ performance in classification issues further. The results show that the algorithm outperforms 10 top-tier optimisation methods, including PSO, ALO, the original GTO and the SCA algorithm. The results highlighted the statistical difference, superiority and importance of the suggested feature selection strategies.

Keywords –Data mining, hybrid feature selection, machine learning, genetic algorithm, artificial gorilla tro ops optimizer.

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