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A Novel Method Based on Nonlinear Binary Grasshopper Whale optimization algorithm for Feature Selection

Journal of bionic Engineering 2023年第1期20卷 237-252页

作者： Lingling Fang Xiyue Liang Department of Computing and Information Technology Liaoning Normal UniversityDalian116081China

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.

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

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