Improved Henry gas optimization for predicting high-income Factors

Authors

S. EL-gazzar, Department of Information Systems, Faculty of Computer Science and Information Systems, Menoufiya University, Shebin El-Koom, EgyptFollow
H. Abdel-kader, Department of Information Systems, Faculty of Computer Science and Information Systems, Menoufiya University, Shebin El-Koom, EgyptFollow
A. Haroon, Department of Information Systems, Faculty of Computer Science and Information Systems, Menoufiya University, Shebin El-Koom, EgyptFollow

Abstract

The Henry gas solubility optimization (HGSO) is a meta-heuristic algorithm based on Henry’s law. In this paper, β–hill climbing operator is introduced to enhance the ability of the local search, which improves the shortcoming of the original HGSO algorithm. The improved Henry gas solubility optimization algorithm (βHC-HGSO) is based on the β -hill climbing local search, which is used for selecting a subset of relevant features for high income to improve the classification accuracy. The random forest (RF) expert system was employed to explain the performance of the proposed algorithm. According to empirical research, the performance of the improved Henry gas solubility(βHC- HGSO) is better than the original algorithm.

Recommended Citation

EL-gazzar, S.; Abdel-kader, H.; and Haroon, A. (2023) "Improved Henry gas optimization for predicting high-income Factors," Information Sciences Letters: Vol. 12 : Iss. 10 , PP -.
Available at: https://digitalcommons.aaru.edu.jo/isl/vol12/iss10/30