基于改进秃鹰搜索算法的同步优化特征选择
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1.三明学院;2.东北林业大学机电工程学院

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TP273

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福建省教育厅中青年教师教育科研项目;三明市科技计划引导性项目;三明学院引进高层次人才科研启动经费项目


Simultaneous Feature Selection Optimization based on Improved Bald Eagle Search Algorithm
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Sanming University

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    摘要:

    针对传统支持向量机在封装式特征选择中分类效果差、子集选取冗余、计算性能易受核函数参数影响 的不足, 利用元启发式优化算法对其进行同步优化. 本文首先利用莱维飞行策略与模拟退火机制对秃鹰搜索算法 的局部搜索能力与勘探利用解空间能力进行改进, 标准函数的测试结果证明其改进有效; 其次将支持向量机核函 数参数作为待优化目标, 利用改进后的算法在封装式特征选择模型中搜寻最优核函数参数,同时获得相对应的最 优特征子集; 最后对 UCI 存储库的 12 个标准数据集进行特征选择仿真实验, 在平均分类准确率、所选特征个数 及适应度值上综合评估分析, 实验结果表明本文所提算法可有效降低特征维度, 能够更准确的实现数据分类. 在 空间搜索与求解精度方面较原算法及其他非线性最优化算法表现优秀, 具有一定的工程应用价值.

    Abstract:

    Aiming at the shortcomings of support vector machine in wrapper feature selection, such as poor classification effect, redundant subset selection, and computational performance that are easily affected by kernel function parameters, meta-heuristic optimization algorithm is used to optimize it simultaneously. In this paper, firstly the local search ability and the exploration and utilization solution space ability of the bald eagle search algorithm are improved by using Levy flight strategy and simulated annealing mechanism, the test results of standard function prove that the improvement is effective; secondly the kernel function parameters of support vector machine are taken as the optimization objective, and the improved algorithm is used to search for the optimal kernel function parameters in the wrapper feature selection model and it obtains the corresponding feature subset simultaneously; finally a feature selection simulation is performed on the 12 standard data sets of the UCI repository, and the average classification accuracy, the number of selected features and the fitness value are comprehensively evaluated and analyzed. The experimental results show that the algorithm proposed in this paper can effectively reduce the feature dimension and achieve data classification more accurately. Compared with the original algorithm and other nonlinear optimization algorithms, it has certain engineering application value.

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  • 收稿日期:2020-07-24
  • 最后修改日期:2020-11-23
  • 录用日期:2020-12-03
  • 在线发布日期: 2021-01-04
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