BGOA-TVG: Binary Grasshopper Optimization Algorithm with Time-Varying Gaussian Transfer Functions for Feature Selection

被引:0
|
作者
Li, Mengjun [1 ]
Luo, Qifang [1 ,2 ]
Zhou, Yongquan [1 ,2 ,3 ]
机构
[1] Guangxi Minzu Univ, Coll Artificial Intelligence, Nanning 530006, Peoples R China
[2] Guangxi Key Labs Hybrid Computat & IC Design Anal, Nanning 530006, Peoples R China
[3] Univ Kebangsaan Malaysia, Fac Informat Sci & Technol, Bangi 43600, Selangor, Malaysia
基金
中国国家自然科学基金;
关键词
binary grasshopper optimization algorithm; time-varying Gaussian transfer function; UCI dataset; DEAP dataset; EPILEPSY dataset; feature selection; metaheuristic; ARTIFICIAL BEE COLONY; CLASSIFICATION; HYBRID;
D O I
10.3390/biomimetics9030187
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions have the characteristics of a fast convergence speed and a strong global search capability to convert a continuous search space to a binary one. The BGOA-TVG is tested and compared to S-shaped and V-shaped binary grasshopper optimization algorithms and five state-of-the-art swarm intelligence algorithms for feature selection. The experimental results show that the BGOA-TVG has better performance in UCI, DEAP, and EPILEPSY datasets for feature selection.
引用
收藏
页数:38
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