Li Mengjun, Luo Qifang, Zhou Yongquan
College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China.
Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China.
Biomimetics (Basel). 2024 Mar 20;9(3):187. doi: 10.3390/biomimetics9030187.
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.
特征选择旨在选择关键特征，以提高机器学习和数据挖掘中的分类准确率。本文提出了一种使用时变高斯传递函数的新型二进制蚱蜢优化算法（BGOA-TVG）用于特征选择。与传统的S形和V形传递函数相比，所提出的高斯时变传递函数具有收敛速度快和全局搜索能力强的特点，能够将连续搜索空间转换为二进制搜索空间。对BGOA-TVG进行了测试，并与S形和V形二进制蚱蜢优化算法以及五种先进的群智能算法进行了特征选择方面的比较。实验结果表明，BGOA-TVG在UCI、DEAP和癫痫数据集的特征选择方面具有更好的性能。
