A Novel Method Based on Nonlinear Binary Grasshopper Whale Optimization Algorithm for Feature Selection
作者机构：Department of Computing and Information TechnologyLiaoning Normal UniversityDalian116081China
出 版 物：《Journal of Bionic Engineering》 (仿生工程学报（英文版）)
年 卷 期：2023年第20卷第1期
页 面：237-252页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by Natural Science Foundation of Liaoning Province under Grant 2021-MS-272 Educational Committee project of Liaoning Province under Grant LJKQZ2021088
主 题：Feature selection Hybrid bionic optimization algorithm Biomimetic position updating strategy Nature-inspired algorithm-High-dimensional UCI datasets-Multi-modal medical datasets
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 *** 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 *** 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 *** distinct high-dimensional UCI datasets,the multi-modal Parkinson s speech datasets,and the COVID-19 symptom dataset are used to validate the proposed *** 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 ***,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,*** results reveal that the proposed NL-BGWOA has comprehensive superiority in solving the FS problem of high-dimensional data.
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