Binary dynamic stochastic search algorithm with support vector regression for feature selection in low-velocity impact localization problem

Liu, Qi; Wang, Fengde; Xiao, Wensheng; Cui, Junguo

doi:10.1016/j.engappai.2023.106554

Cited by 4 publications

(1 citation statement)

References 62 publications

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“…The authors of [ 56 , 57 , 58 ] provided a new method to extract optimal feature subset to enhance accuracy of the calculation. Others have proposed correlation feature selection [ 59 ] and in-depth analyses on the usage of searching methods like the best-first, greedy step-wise, genetic, linear forward selection, and rank searches [ 60 ].…”

Section: Feature Selection Problemmentioning

confidence: 99%

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

Li,

Luo,

Zhou

2024

Biomimetics

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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.

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