Compound strategy based binary willow catkin optimization;
feature selection

doi:10.1007/s10586-024-04879-5

Compound strategy based binary willow catkin optimization; feature selection

2025-01-21

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DOI：10.1007/s10586-024-04879-5

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Feature selection is a crucial preprocessing technique that enhances the efficiency and accuracy of machine learning models by removing irrelevant and redundant features, thus reducing computational complexity and storage costs. However, existing binary swarm intelligence algorithms often encounter challenges such as getting trapped in local optima and lacking sufficient convergence per; mance. This study proposes a Compound Binary Willow Catkin Optimization (CBWCO) algorithm specifically designed; feature selection tasks. This paper extends the standard Willow Catkin Optimization (WCO) to its binary; m, integrating a VU-shaped transfer function and a compound mutation strategy to improve adaptability; both high- and low-dimensional data while maintaining population diversity and strengthening global search capability. Experimental results on twelve benchmark datasets with different dimensions and fields from the UCI open-source databases show that CBWCO outper; ms BWCO(S-shaped), Binary Grey Wolf Optimization (BGWO), Binary Particle Swarm Optimization (BPSO), Binary Differential Evolution and Genetic Algorithm(GA) in terms of convergence speed and classification per; mance across most datasets. These results highlight CBWCO's potential advantages in feature selection and lay the groundwork for its future applications in fields such as signal recognition, fault diagnosis, and medical rehabilitation.

Keywords:

Feature selection

Classification

Binary willow catkin optimization

Transfer function

mutation strategy

UCI

Compound strategy based binary willow catkin optimization; feature selection

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