Journal of Bionic Engineering ›› 2023, Vol. 20 ›› Issue (1): 237-252.doi: 10.1007/s42235-022-00253-6

• • 上一篇    下一篇

A Novel Method Based on Nonlinear Binary Grasshopper Whale Optimization Algorithm for Feature Selection

Lingling Fang1; Xiyue Liang1   

  1. 1 Department of Computing and Information Technology, Liaoning Normal University, Dalian 116081, China
  • 出版日期:2023-01-10 发布日期:2023-02-16
  • 通讯作者: Lingling Fang E-mail:fanglingling@lnnu.edu.cn
  • 作者简介:Lingling Fang1; Xiyue Liang1

A Novel Method Based on Nonlinear Binary Grasshopper Whale Optimization Algorithm for Feature Selection

Lingling Fang1; Xiyue Liang1   

  1. 1 Department of Computing and Information Technology, Liaoning Normal University, Dalian 116081, China
  • Online:2023-01-10 Published:2023-02-16
  • Contact: Lingling Fang E-mail:fanglingling@lnnu.edu.cn
  • About author:Lingling Fang1; Xiyue Liang1

摘要: Feature Selection (FS) is considered as an important preprocessing step in data mining and is used to remove redundant or unrelated features from high-dimensional data. Most optimization algorithms for FS problems are not balanced in search. A hybrid algorithm called nonlinear binary grasshopper whale optimization algorithm (NL-BGWOA) is proposed to solve the problem in this paper. In the proposed method, a new position updating strategy combining the position changes of whales and grasshoppers population is expressed, which optimizes the diversity of searching in the target domain. Ten distinct high-dimensional UCI datasets, the multi-modal Parkinson's speech datasets, and the COVID-19 symptom dataset are used to validate the proposed method. It has been demonstrated that the proposed NL-BGWOA performs well across most of high-dimensional datasets, which shows a high accuracy rate of up to 0.9895. Furthermore, the experimental results on the medical datasets also demonstrate the advantages of the proposed method in actual FS problem, including accuracy, size of feature subsets, and fitness with best values of 0.913, 5.7, and 0.0873, respectively. The results reveal that the proposed NL-BGWOA has comprehensive superiority in solving the FS problem of high-dimensional data.

关键词: Feature selection , · Hybrid bionic optimization algorithm , · Biomimetic position updating strategy , · Nature-inspired algorithm , · High-dimensional UCI datasets , · Multi-modal medical datasets

Abstract: Feature Selection (FS) is considered as an important preprocessing step in data mining and is used to remove redundant or unrelated features from high-dimensional data. Most optimization algorithms for FS problems are not balanced in search. A hybrid algorithm called nonlinear binary grasshopper whale optimization algorithm (NL-BGWOA) is proposed to solve the problem in this paper. In the proposed method, a new position updating strategy combining the position changes of whales and grasshoppers population is expressed, which optimizes the diversity of searching in the target domain. Ten distinct high-dimensional UCI datasets, the multi-modal Parkinson's speech datasets, and the COVID-19 symptom dataset are used to validate the proposed method. It has been demonstrated that the proposed NL-BGWOA performs well across most of high-dimensional datasets, which shows a high accuracy rate of up to 0.9895. Furthermore, the experimental results on the medical datasets also demonstrate the advantages of the proposed method in actual FS problem, including accuracy, size of feature subsets, and fitness with best values of 0.913, 5.7, and 0.0873, respectively. The results reveal that the proposed NL-BGWOA has comprehensive superiority in solving the FS problem of high-dimensional data.

Key words: Feature selection , · Hybrid bionic optimization algorithm , · Biomimetic position updating strategy , · Nature-inspired algorithm , · High-dimensional UCI datasets , · Multi-modal medical datasets