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基于遗传乌燕鸥算法的同步优化特征选择

贾鹤鸣 李瑶 孙康健

贾鹤鸣, 李瑶, 孙康健. 基于遗传乌燕鸥算法的同步优化特征选择. 自动化学报, 2022, 48(6): 1601−1615 doi: 10.16383/j.aas.c200322
引用本文: 贾鹤鸣, 李瑶, 孙康健. 基于遗传乌燕鸥算法的同步优化特征选择. 自动化学报, 2022, 48(6): 1601−1615 doi: 10.16383/j.aas.c200322
Jia He-Ming, Li Yao, Sun Kang-Jian. Simultaneous feature selection optimization based on hybrid sooty tern optimization algorithm and genetic algorithm. Acta Automatica Sinica, 2022, 48(6): 1601−1615 doi: 10.16383/j.aas.c200322
Citation: Jia He-Ming, Li Yao, Sun Kang-Jian. Simultaneous feature selection optimization based on hybrid sooty tern optimization algorithm and genetic algorithm. Acta Automatica Sinica, 2022, 48(6): 1601−1615 doi: 10.16383/j.aas.c200322

基于遗传乌燕鸥算法的同步优化特征选择

doi: 10.16383/j.aas.c200322
基金项目: 福建省自然科学基金项目(2021J011128), 三明学院国家基金培育计划项目(PYT2105)资助
详细信息
    作者简介:

    贾鹤鸣:三明学院信息工程学院教授. 主要研究方向为智能优化与图像处理和非线性控制理论与应用. 本文通信作者. E-mail: jiaheminglucky99@126.com

    李瑶:东北林业大学机电工程学院硕士研究生. 主要研究方向为智能优化与特征选择. E-mail: liyao@nefu.edu.cn

    孙康健:东北林业大学机电工程学院硕士研究生. 主要研究方向为智能优化与特征选择. E-mail: sunkangjian@nefu.edu.cn

Simultaneous Feature Selection Optimization Based on Hybrid Sooty Tern Optimization Algorithm and Genetic Algorithm

Funds: Supported by Fujian Natural Science Foundation Project (2021J011128) and National Fund Cultivation Program of Sanming University (PYT2105)
More Information
    Author Bio:

    JIA He-Ming Professor at the School of Information Engineering, Sanming University. His research interest covers intelligent optimization and image processing, nonlinear control theory and application. Corresponding author of this paper

    LI Yao Master student at the School of Mechanical and Electrical Engineering, Northeast Forestry University. Her research interest covers intelligent optimization and feature selection

    SUN Kang-Jian Master student at the School of Mechanical and Electrical Engineering, Northeast Forestry University. His research interest covers intelligent optimization and image processing

  • 摘要: 针对传统支持向量机方法用于数据分类存在分类精度低的不足问题, 将支持向量机分类方法与特征选择同步结合, 并利用智能优化算法对算法参数进行优化研究. 首先将遗传算法(Genetic algorithm, GA)和乌燕鸥优化算法(Sooty tern optimization algorithm, STOA)进行混合, 先通过对平均适应度值进行评估, 当个体的适应度函数值小于平均值时采用遗传算法对其进行局部搜索的加强, 否则进行乌燕鸥本体优化过程, 同时将支持向量机内核函数和特征选择目标共同作为优化对象, 利用改进后的STOA-GA寻找最适应解, 获得所选的特征分类结果. 其次, 通过16组经典UCI数据集和实际乳腺癌数据集进行数据分类研究, 在最佳适应度值、所选特征个数、特异性、敏感性和算法耗时方面进行对比研究, 实验结果表明, 该算法可以更加准确地处理数据, 避免冗余特征干扰, 在数据挖掘领域具有更广阔的工程应用前景.
  • 图  1  STOA-GA的流程图

    Fig.  1  Framework of the STOA-GA

    图  2  SVM最优超平面示意图

    Fig.  2  SVM optimal hyperplane diagram

    图  3  每个个体的搜索维度示意图

    Fig.  3  Schematic of search dimensions for each individual

    图  4  混合算法的流程图

    Fig.  4  Hybrid algorithm flow chart

    图  5  各算法分类精度平均值

    Fig.  5  The average accuracy of each algorithm

    图  6  各算法所选特征平均值

    Fig.  6  The average value of the selected features of each algorithm

    图  7  各算法适应度函数收敛曲线图

    Fig.  7  The convergence curve of fitness of each algorithm

    表  1  实验数据集

    Table  1  The data sets used in the experiments

    序号数据集特征数样本数类别数
    1Iris41503
    2Immunotherapy8902
    3Tic-Tac-Toe99582
    4Wine131783
    5Zoo171017
    6Hepatitis191552
    7Forest Types273264
    8Dermatology333666
    9Ionosphere343512
    10Divorce Predictors541702
    11Urban Land Cover1481689
    12SCADI206707
    13Arrhythmia27945216
    14LSVT Voice Rehabilitation3091262
    15Detect Malacious Executable (AntiVirus)5133732
    16Parkinson's Disease7547562
    下载: 导出CSV

    表  2  对比算法的参数

    Table  2  Parameters of the compared algorithms

    算法参数设定值
    STOA-GA控制变量${C_f}$2
    随机变量${C_B}$[0, 0.5]
    螺旋常数$u,v$1
    交叉概率$Pc$0.95
    变异概率$Pm$0.05
    STOA[29]控制变量${C_f}$2
    随机变量${C_B}$[0, 0.5]
    螺旋常数$u,v$1
    GA[32]交叉概率$Pc$0.95
    变异概率$Pm$0.05
    PSO[15]学习因子${c_1},{c_2}$1.5
    权重因子$\omega $0.75
    速度$v$[0, 1]
    常数${\rm{a}}$2
    SHO[16]控制因子$h$[0, 5]
    随机向量$M$[0.5, 1]
    EPO[17]移动参数$M$2
    控制参数$f$[2, 3]
    控制参数$l$[1.5, 2]
    下载: 导出CSV

    表  3  各算法运行时间平均值(s)

    Table  3  The average time of each algorithm (s)

    数据集STOA-GASTOAGAPSOEPOSHO
    Iris12.7112.0918.4914.3615.2814.41
    Immunotherapy7.096.9513.897.5210.239.22
    Tic-Tac-Toe169.67169.29180.41171.52181.08189.53
    Wine40.2339.6750.3439.6748.8151.26
    Zoo16.9516.2519.9616.4920.3819.45
    Hepatitis22.9022.4826.6224.5128.2427.51
    Forest Types120.54120.35122.63115.57120.82119.60
    Dermatology97.6993.76120.2897.74117.21115.87
    Ionosphere74.7471.9184.9285.0986.9681.92
    Divorce Predictors29.1727.2645.2432.6342.4132.87
    Urban Land Cover186.15185.79186.19188.76192.15207.50
    SCADI45.5442.2761.1858.7261.4761.06
    Arrhythmia4132.404286.945382.194802.384582.085129.23
    LSVT Voice110.32104.39110.07105.10103.71102.43
    Detect Malacious151.86109.94466.58837.45664.20829.48
    Parkinson's Disease138.06135.02149.29145.62146.73144.75
    下载: 导出CSV

    表  4  各算法适应度函数平均值

    Table  4  The average fitness of each algorithm

    数据集STOA-GASTOAGAPSOEPOSHO
    Iris0.01380.02310.06370.12940.02770.0202
    Immunotherapy0.1010.14310.21250.21290.21630.2172
    Tic-Tac-Toe0.0040.17310.34770.23050.01180.2164
    Wine0.02820.05930.43520.29450.29250.2849
    Zoo0.01310.05630.12920.07440.03510.1767
    Hepatitis0.25510.31230.45320.41560.41740.2549
    Forest Types0.12560.19530.58170.58410.27990.1742
    Dermatology0.02210.03840.46470.03670.06140.6913
    Ionosphere0.03340.06810.35470.35080.05050.3561
    Divorce Predictors0.01130.02260.20880.02620.02260.3269
    Urban Land Cover0.30120.44430.58070.82440.62570.6422
    SCADI0.13160.16070.45730.16070.16470.5844
    Arrhythmia0.25640.26030.28010.26990.27660.2823
    LSVT Voice0.33490.33500.33570.33490.33520.3352
    Detect Malacious0.00480.01040.17770.01290.01240.1855
    Parkinson's Disease0.26280.28380.39360.46760.28170.2872
    下载: 导出CSV

    表  5  各算法适应度函数标准差

    Table  5  The standard deviation of fitness of each algorithm

    数据集STOA-GASTOAGAPSOEPOSHO
    Iris0.00420.00670.03220.10.00670.0089
    Immunotherapy0.02060.10310.18210.20320.19880.0976
    Tic-Tac-Toe0.00670.01270.01480.02860.01740.0053
    Wine0.01010.01530.02180.02790.01010.0129
    Zoo0.00770.010.02720.03010.00890.0103
    Hepatitis0.02880.04290.21570.28910.10380.0302
    Forest Types0.01770.02820.013900.02340.0356
    Dermatology0.01330.02110.30360.01670.03140.3781
    Ionosphere00.00380.02870.01070.01830.0046
    Divorce Predictors0.00670.01330.13670.00830.00870.1492
    Urban Land Cover0.10440.12530.25170.10210.20890.2182
    SCADI0.06810.13720.18790.07060.10410.1645
    Arrhythmia0.12670.11460.10280.13820.12560.1474
    LSVT Voice0.001000.001200.00170.0039
    Detect Malacious00.00240.01470.003700.0183
    Parkinson's Disease0.09230.10320.13730.21040.13420.1567
    下载: 导出CSV

    表  6  各算法特异性(%)

    Table  6  The specificity of each algorithm (%)

    数据集DTNBKNNSVM本文方法
    Ionosphere89.6479.6796.0393.8497.67
    Tic-Tac-Toe90.5684.3299.4398.54100
    Hepatitis72.1760.5178.3474.2377.34
    Immunotherapy80.8976.5586.5684.9990.76
    Divorce Predictors91.3285.3398.7693.67100
    下载: 导出CSV

    表  8  各算法精确度(%)

    Table  8  The accuracy of each algorithm (%)

    数据集DTNBKNNSVM本文方法
    Ionosphere89.2378.1593.4792.1896.87
    Tic-Tac-Toe89.6383.9297.1495.26100
    Hepatitis72.0457.4377.5472.3175.19
    Immunotherapy79.6574.6985.7182.9790.00
    Divorce Predictors90.1884.2297.9192.36100
    下载: 导出CSV

    表  7  各算法敏感性(%)

    Table  7  The sensitivity of each algorithm (%)

    数据集DTNBKNNSVM本文方法
    Ionosphere88.6776.3791.7890.1395.48
    Tic-Tac-Toe87.1383.6795.4393.2799.54
    Hepatitis71.9655.8276.7169.3873.11
    Immunotherapy79.3473.1384.5280.2689.05
    Divorce Predictors89.0384.0195.3991.6798.75
    下载: 导出CSV

    表  9  乳腺癌数据集特征信息

    Table  9  The breast cancer data set feature information

    序号英文简称说明
    1Age年龄, [10, 99]岁, 每10岁为1个区间, 共9个区间
    2Menopause绝经期, 分为未绝经、40岁之后绝经、40岁之前绝经
    3Tumor-size肿瘤大小, [0, 59]mm, 每5为1个区间, 共12个区间
    4Inv-nodes淋巴结个数, [0, 39], 每3个为1个区间, 共13个区间
    5Node-caps结节冒有无
    6Deg-malig肿瘤恶性程度, 分为1、2、3三种, 3恶性程度最高
    7Breast分为左和右两部分
    8Breast-quad分为左上、左下、右上、右下4个区域
    9Irradiat是否有放射性治疗经历
    下载: 导出CSV

    表  10  STOA-GA算法的10次实验运行结果

    Table  10  The results of 10 experiments of STOA-GA

    序号分类准确
    率 (%)
    选择特征
    个数
    适应度值时间 (s)特异性
    (%)
    敏感性
    (%)
    197.6250.029164.0897.8796.83
    297.5640.028663.8397.7596.12
    396.7450.037835.5797.9891.53
    497.4850.030564.1598.6496.05
    598.2140.022262.0998.5197.66
    697.5640.028660.4997.8796.83
    797.6650.028764.7197.8097.45
    897.9840.024462.0198.0397.89
    996.2850.042464.7198.3791.31
    1098.0340.023968.2998.3797.76
    下载: 导出CSV

    表  11  10次实验均入选的特征

    Table  11  The selected feature of 10 experiments

    序号特征
    3肿瘤大小
    4淋巴结个数
    5结节冒有无
    6肿瘤恶性程度
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-05-18
  • 网络出版日期:  2022-05-19
  • 刊出日期:  2022-06-02

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