Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (5): 1355-1366.DOI: 10.11772/j.issn.1001-9081.2021030497
Special Issue: 人工智能
• Artificial intelligence • Previous Articles Next Articles
Lin SUN1,2(), Jing ZHAO1, Jiucheng XU1,2, Xinya WANG1
Received:
2021-04-02
Revised:
2021-09-15
Accepted:
2021-09-22
Online:
2022-06-11
Published:
2022-05-10
Contact:
Lin SUN
About author:
SUN Lin, born in 1979,Ph. D.,associate professor. His researchinterests include granular computing,data mining,machine learning,bioinformatics.Supported by:
通讯作者:
孙林
作者简介:
孙林(1979—),男,河南南阳人,副教授,博士,CCF会员,主要研究方向:粒计算、数据挖掘、机器学习、生物信息学 sunlin@htu.edu.cn基金资助:
CLC Number:
Lin SUN, Jing ZHAO, Jiucheng XU, Xinya WANG. Feature selection algorithm based on neighborhood rough set and monarch butterfly optimization[J]. Journal of Computer Applications, 2022, 42(5): 1355-1366.
孙林, 赵婧, 徐久成, 王欣雅. 基于邻域粗糙集和帝王蝶优化的特征选择算法[J]. 《计算机应用》唯一官方网站, 2022, 42(5): 1355-1366.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021030497
函数 | 公式 | 变量范围 | 理论最优值 |
---|---|---|---|
Sphere | [-5.12,5.12] D | 0 | |
Schwefel2. 22 | [-10,10] D | 0 | |
Step | [-100,100] D | 0 | |
Griank | [-600,600] D | 0 | |
Ackley | [-30,30] D | 0 | |
Rastrigin | [-5.12,5.12] D | 0 |
Tab. 1 Information of six benchmark functions
函数 | 公式 | 变量范围 | 理论最优值 |
---|---|---|---|
Sphere | [-5.12,5.12] D | 0 | |
Schwefel2. 22 | [-10,10] D | 0 | |
Step | [-100,100] D | 0 | |
Griank | [-600,600] D | 0 | |
Ackley | [-30,30] D | 0 | |
Rastrigin | [-5.12,5.12] D | 0 |
函数 | 算法 | 最优值 | 最差值 | 平均值 | 标准差 | 运行时间/s |
---|---|---|---|---|---|---|
f 1 | PSO | 4.23E | 6.12E+01 | 5.09E+01 | 4.06E+00 | 18 |
MBO | 7.90E | 2.15E+02 | 6.67E+01 | 7.92E+01 | 177 | |
BMBOM | 4.75E-08 | 8.19E-05 | 8.66E-06 | 1.62E-05 | 516 | |
f2 | PSO | 7.76E+03 | 1.57E+04 | 1.20E+04 | 1.99E+03 | 54 |
MBO | 3.91E+02 | 5.06E+04 | 2.37E+04 | 1.49E+04 | 142 | |
BMBOM | 0 | 1.36E+02 | 5.72E+01 | 4.58E+01 | 430 | |
f3 | PSO | 9.33E+03 | 1.60E+04 | 1.24E+04 | 1.63E+03 | 53 |
MBO | 0 | 7.28E+04 | 2.38E+04 | 2.71E+04 | 176 | |
BMBOM | 0 | 8.28E+02 | 2.76E+01 | 1.51E+02 | 388 | |
f4 | PSO | 6.57E+05 | 1.43E+06 | 1.15E+06 | 1.66E+05 | 28 |
MBO | 1 | 6.36E+02 | 1.80E+02 | 2.29E+02 | 175 | |
BMBOM | 1 | 3.18E+01 | 2.03E+00 | 5.62E+00 | 395 | |
f5 | PSO | 4.22E+01 | 1.35E+02 | 6.09E+01 | 2.72E+01 | 19 |
MBO | 1.32E-01 | 8.95E+01 | 3.46E+01 | 2.36E+01 | 138 | |
BMBOM | 1.30E-04 | 9.65E-03 | 1.92E-03 | 2.16E-03 | 341 | |
f6 | PSO | 2.06E+02 | 2.66E+02 | 2.50E+02 | 1.37E+01 | 54 |
MBO | 2.32E-05 | 4.45E+02 | 1.68E+02 | 1.45E+02 | 177 | |
BMBOM | 2.81E-07 | 1.12E+02 | 6.34E+00 | 2.27E+01 | 363 |
Tab. 2 Experimental results of three optimization algorithms on 30-dimensions of six benchmark functions
函数 | 算法 | 最优值 | 最差值 | 平均值 | 标准差 | 运行时间/s |
---|---|---|---|---|---|---|
f 1 | PSO | 4.23E | 6.12E+01 | 5.09E+01 | 4.06E+00 | 18 |
MBO | 7.90E | 2.15E+02 | 6.67E+01 | 7.92E+01 | 177 | |
BMBOM | 4.75E-08 | 8.19E-05 | 8.66E-06 | 1.62E-05 | 516 | |
f2 | PSO | 7.76E+03 | 1.57E+04 | 1.20E+04 | 1.99E+03 | 54 |
MBO | 3.91E+02 | 5.06E+04 | 2.37E+04 | 1.49E+04 | 142 | |
BMBOM | 0 | 1.36E+02 | 5.72E+01 | 4.58E+01 | 430 | |
f3 | PSO | 9.33E+03 | 1.60E+04 | 1.24E+04 | 1.63E+03 | 53 |
MBO | 0 | 7.28E+04 | 2.38E+04 | 2.71E+04 | 176 | |
BMBOM | 0 | 8.28E+02 | 2.76E+01 | 1.51E+02 | 388 | |
f4 | PSO | 6.57E+05 | 1.43E+06 | 1.15E+06 | 1.66E+05 | 28 |
MBO | 1 | 6.36E+02 | 1.80E+02 | 2.29E+02 | 175 | |
BMBOM | 1 | 3.18E+01 | 2.03E+00 | 5.62E+00 | 395 | |
f5 | PSO | 4.22E+01 | 1.35E+02 | 6.09E+01 | 2.72E+01 | 19 |
MBO | 1.32E-01 | 8.95E+01 | 3.46E+01 | 2.36E+01 | 138 | |
BMBOM | 1.30E-04 | 9.65E-03 | 1.92E-03 | 2.16E-03 | 341 | |
f6 | PSO | 2.06E+02 | 2.66E+02 | 2.50E+02 | 1.37E+01 | 54 |
MBO | 2.32E-05 | 4.45E+02 | 1.68E+02 | 1.45E+02 | 177 | |
BMBOM | 2.81E-07 | 1.12E+02 | 6.34E+00 | 2.27E+01 | 363 |
序号 | 名称 | 特征数 | 样本数 |
---|---|---|---|
1 | Breastcancer | 9 | 699 |
2 | Congress | 16 | 435 |
3 | Heart | 13 | 270 |
4 | Hepatitis | 19 | 155 |
5 | Ionosphere | 34 | 351 |
6 | Lungcancer | 56 | 32 |
7 | Lymphography | 18 | 148 |
8 | Sonar | 60 | 208 |
9 | Spect | 22 | 267 |
10 | Tic-tac-toe | 9 | 958 |
11 | Vote | 16 | 300 |
12 | Waveform | 40 | 5 000 |
13 | Wine | 13 | 178 |
14 | Zoo | 16 | 101 |
Tab. 3 Information of fourteen UCI datasets
序号 | 名称 | 特征数 | 样本数 |
---|---|---|---|
1 | Breastcancer | 9 | 699 |
2 | Congress | 16 | 435 |
3 | Heart | 13 | 270 |
4 | Hepatitis | 19 | 155 |
5 | Ionosphere | 34 | 351 |
6 | Lungcancer | 56 | 32 |
7 | Lymphography | 18 | 148 |
8 | Sonar | 60 | 208 |
9 | Spect | 22 | 267 |
10 | Tic-tac-toe | 9 | 958 |
11 | Vote | 16 | 300 |
12 | Waveform | 40 | 5 000 |
13 | Wine | 13 | 178 |
14 | Zoo | 16 | 101 |
数据集 | FSNB | ARRSFA | PSORSFS | FSARSR | QCSIA_FS | DQBGOA_MR |
---|---|---|---|---|---|---|
Average | 94.60 | 81.34 | 81.07 | 80.62 | 80.82 | 83.04 |
Breastcancer | 95.67 | 92.61 | 92.30 | 91.28 | 92.90 | 94.71 |
Hepatitis | 95.04 | 83.25 | 82.10 | 82.75 | 82.75 | 83.75 |
Ionosphere | 95.04 | 82.02 | 83.17 | 83.97 | 82.06 | 84.71 |
Lungcancer | 89.15 | 65.80 | 69.00 | 62.42 | 60.92 | 69.19 |
Lymphography | 91.62 | 73.97 | 73.70 | 74.13 | 74.16 | 75.43 |
Spect | 94.77 | 78.16 | 78.48 | 75.83 | 77.24 | 79.07 |
Tic-tac-toe | 95.03 | 75.62 | 79.53 | 78.72 | 81.73 | 82.48 |
Vote | 95.03 | 95.37 | 95.41 | 96.32 | 95.74 | 95.89 |
Wine | 97.53 | 72.42 | 67.64 | 70.25 | 71.76 | 74.24 |
Zoo | 97.14 | 94.14 | 89.35 | 90.55 | 88.91 | 90.89 |
Tab. 4 Experimental results of FSNB and five optimized feature selection algorithms based on rough sets on classification accuracy
数据集 | FSNB | ARRSFA | PSORSFS | FSARSR | QCSIA_FS | DQBGOA_MR |
---|---|---|---|---|---|---|
Average | 94.60 | 81.34 | 81.07 | 80.62 | 80.82 | 83.04 |
Breastcancer | 95.67 | 92.61 | 92.30 | 91.28 | 92.90 | 94.71 |
Hepatitis | 95.04 | 83.25 | 82.10 | 82.75 | 82.75 | 83.75 |
Ionosphere | 95.04 | 82.02 | 83.17 | 83.97 | 82.06 | 84.71 |
Lungcancer | 89.15 | 65.80 | 69.00 | 62.42 | 60.92 | 69.19 |
Lymphography | 91.62 | 73.97 | 73.70 | 74.13 | 74.16 | 75.43 |
Spect | 94.77 | 78.16 | 78.48 | 75.83 | 77.24 | 79.07 |
Tic-tac-toe | 95.03 | 75.62 | 79.53 | 78.72 | 81.73 | 82.48 |
Vote | 95.03 | 95.37 | 95.41 | 96.32 | 95.74 | 95.89 |
Wine | 97.53 | 72.42 | 67.64 | 70.25 | 71.76 | 74.24 |
Zoo | 97.14 | 94.14 | 89.35 | 90.55 | 88.91 | 90.89 |
数据集 | FSNB | ARRSFA | PSORSFS | FSARSR | QCSIA_FS | DQBGOA_MR |
---|---|---|---|---|---|---|
Average | 8.49 | 7.23 | 9.02 | 8.65 | 7.34 | 5.92 |
Breastcancer | 5.75 | 3.70 | 3.80 | 4.00 | 3.50 | 3.00 |
Hepatitis | 14.65 | 4.10 | 6.20 | 5.60 | 5.00 | 3.70 |
Ionosphere | 14.65 | 4.60 | 10.20 | 8.50 | 6.10 | 4.10 |
Lungcancer | 6.30 | 7.90 | 19.40 | 17.10 | 11.60 | 4.30 |
Lymphography | 14.75 | 9.50 | 8.60 | 7.80 | 7.80 | 7.10 |
Spect | 5.00 | 14.00 | 15.10 | 16.10 | 14.70 | 14.00 |
Tic-tac-toe | 5.00 | 7.20 | 7.40 | 8.00 | 7.00 | 7.00 |
Vote | 5.00 | 9.50 | 8.80 | 9.60 | 8.10 | 7.50 |
Wine | 7.20 | 3.20 | 3.60 | 3.60 | 3.10 | 3.00 |
Zoo | 6.55 | 8.60 | 7.10 | 6.20 | 6.50 | 5.50 |
Tab. 5 Experimental results of FSNB and five optimized feature selection algorithms based on rough set on number of selected features
数据集 | FSNB | ARRSFA | PSORSFS | FSARSR | QCSIA_FS | DQBGOA_MR |
---|---|---|---|---|---|---|
Average | 8.49 | 7.23 | 9.02 | 8.65 | 7.34 | 5.92 |
Breastcancer | 5.75 | 3.70 | 3.80 | 4.00 | 3.50 | 3.00 |
Hepatitis | 14.65 | 4.10 | 6.20 | 5.60 | 5.00 | 3.70 |
Ionosphere | 14.65 | 4.60 | 10.20 | 8.50 | 6.10 | 4.10 |
Lungcancer | 6.30 | 7.90 | 19.40 | 17.10 | 11.60 | 4.30 |
Lymphography | 14.75 | 9.50 | 8.60 | 7.80 | 7.80 | 7.10 |
Spect | 5.00 | 14.00 | 15.10 | 16.10 | 14.70 | 14.00 |
Tic-tac-toe | 5.00 | 7.20 | 7.40 | 8.00 | 7.00 | 7.00 |
Vote | 5.00 | 9.50 | 8.80 | 9.60 | 8.10 | 7.50 |
Wine | 7.20 | 3.20 | 3.60 | 3.60 | 3.10 | 3.00 |
Zoo | 6.55 | 8.60 | 7.10 | 6.20 | 6.50 | 5.50 |
数据集 | FSNB | GARS | PSORS | ABCRS | FARS | SSORS | CSRS | HSRS | IRRARS |
---|---|---|---|---|---|---|---|---|---|
Average | 89.70 | 76.49 | 78.72 | 82.57 | 79.81 | 83.22 | 82.59 | 82.48 | 85.79 |
Breastcancer | 95.67 | 87.28 | 91.05 | 96.49 | 92.73 | 95.39 | 93.42 | 97.37 | 97.99 |
Congress | 77.02 | 89.66 | 90.41 | 91.38 | 91.38 | 95.86 | 93.79 | 92.79 | 95.17 |
Heart | 76.98 | 65.06 | 66.41 | 70.15 | 69.41 | 71.57 | 70.15 | 73.13 | 75.18 |
Ionosphere | 95.04 | 79.85 | 80.40 | 84.90 | 80.80 | 84.70 | 85.40 | 84.00 | 86.07 |
Lungcancer | 89.15 | 60.00 | 65.36 | 69.64 | 64.60 | 69.64 | 70.64 | 66.00 | 75.98 |
Waveform | 96.89 | 74.82 | 76.82 | 80.60 | 77.90 | 81.90 | 80.60 | 80.20 | 83.25 |
Zoo | 97.14 | 78.79 | 80.61 | 84.85 | 81.88 | 83.50 | 84.12 | 83.88 | 86.88 |
Tab. 6 Experimental results of FSNB and eight feature selection algorithms combining rough set and optimization algorithm on classification accuracy
数据集 | FSNB | GARS | PSORS | ABCRS | FARS | SSORS | CSRS | HSRS | IRRARS |
---|---|---|---|---|---|---|---|---|---|
Average | 89.70 | 76.49 | 78.72 | 82.57 | 79.81 | 83.22 | 82.59 | 82.48 | 85.79 |
Breastcancer | 95.67 | 87.28 | 91.05 | 96.49 | 92.73 | 95.39 | 93.42 | 97.37 | 97.99 |
Congress | 77.02 | 89.66 | 90.41 | 91.38 | 91.38 | 95.86 | 93.79 | 92.79 | 95.17 |
Heart | 76.98 | 65.06 | 66.41 | 70.15 | 69.41 | 71.57 | 70.15 | 73.13 | 75.18 |
Ionosphere | 95.04 | 79.85 | 80.40 | 84.90 | 80.80 | 84.70 | 85.40 | 84.00 | 86.07 |
Lungcancer | 89.15 | 60.00 | 65.36 | 69.64 | 64.60 | 69.64 | 70.64 | 66.00 | 75.98 |
Waveform | 96.89 | 74.82 | 76.82 | 80.60 | 77.90 | 81.90 | 80.60 | 80.20 | 83.25 |
Zoo | 97.14 | 78.79 | 80.61 | 84.85 | 81.88 | 83.50 | 84.12 | 83.88 | 86.88 |
数据集 | FSNB | GARS | PSORS | ABCRS | FARS | SSORS | CSRS | HSRS | IRRARS |
---|---|---|---|---|---|---|---|---|---|
Average | 7.68 | 710.79 | 602.76 | 635.32 | 619.06 | 596.46 | 665.62 | 585.41 | 376.36 |
Breastcancer | 5.75 | 492.10 | 399.13 | 399.13 | 437.57 | 336.92 | 399.13 | 422.20 | 329.23 |
Congress | 5.05 | 254.91 | 219.24 | 146.16 | 140.51 | 151.38 | 137.90 | 143.12 | 135.29 |
Heart | 9.15 | 140.94 | 120.69 | 117.99 | 120.15 | 112.59 | 124.20 | 116.10 | 113.67 |
Ionosphere | 14.65 | 193.75 | 188.49 | 133.03 | 144.96 | 143.21 | 132.68 | 143.21 | 134.08 |
Lungcancer | 6.30 | 17.15 | 13.92 | 13.86 | 12.48 | 11.62 | 11.52 | 11.74 | 11.52 |
Waveform | 6.30 | 3 815.00 | 3 225.00 | 3 600.00 | 3 435.00 | 3 385.00 | 3 805.00 | 3 220.00 | 1 880.00 |
Zoo | 6.55 | 61.71 | 52.82 | 37.07 | 42.72 | 34.54 | 48.88 | 41.51 | 30.70 |
Tab. 7 Experimental results of FSNB and eight feature selection algorithms combining rough set and optimization algorithm on number of selected features
数据集 | FSNB | GARS | PSORS | ABCRS | FARS | SSORS | CSRS | HSRS | IRRARS |
---|---|---|---|---|---|---|---|---|---|
Average | 7.68 | 710.79 | 602.76 | 635.32 | 619.06 | 596.46 | 665.62 | 585.41 | 376.36 |
Breastcancer | 5.75 | 492.10 | 399.13 | 399.13 | 437.57 | 336.92 | 399.13 | 422.20 | 329.23 |
Congress | 5.05 | 254.91 | 219.24 | 146.16 | 140.51 | 151.38 | 137.90 | 143.12 | 135.29 |
Heart | 9.15 | 140.94 | 120.69 | 117.99 | 120.15 | 112.59 | 124.20 | 116.10 | 113.67 |
Ionosphere | 14.65 | 193.75 | 188.49 | 133.03 | 144.96 | 143.21 | 132.68 | 143.21 | 134.08 |
Lungcancer | 6.30 | 17.15 | 13.92 | 13.86 | 12.48 | 11.62 | 11.52 | 11.74 | 11.52 |
Waveform | 6.30 | 3 815.00 | 3 225.00 | 3 600.00 | 3 435.00 | 3 385.00 | 3 805.00 | 3 220.00 | 1 880.00 |
Zoo | 6.55 | 61.71 | 52.82 | 37.07 | 42.72 | 34.54 | 48.88 | 41.51 | 30.70 |
数据集 | 分类精度/% | 适应度值 | 所选特征数 | 运行时间/s | ||||
---|---|---|---|---|---|---|---|---|
FSNM | FSNB | FSNM | FSNB | FSNM | FSNB | FSNM | FSNB | |
Average | 87.47 | 91.51 | 0.274 8 | 0.231 3 | 10.45 | 8.64 | 251 | 198 |
Breastcancer | 95.24 | 95.67 | 0.370 8 | 0.342 8 | 6.25 | 5.75 | 119 | 118 |
Congress | 72.68 | 77.02 | 0.208 9 | 0.209 0 | 5.00 | 5.05 | 55 | 55 |
Heart | 75.22 | 76.98 | 0.401 9 | 0.408 3 | 8.15 | 9.15 | 33 | 33 |
Hepatitis | 94.64 | 95.04 | 0.277 5 | 0.272 0 | 15.40 | 14.65 | 1 075 | 1 073 |
Ionosphere | 94.10 | 95.04 | 0.276 1 | 0.272 0 | 15.10 | 14.65 | 1 074 | 1 065 |
Lungcancer | 73.77 | 89.15 | 0.138 6 | 0.105 0 | 11.00 | 6.30 | 6 | 5 |
Lymphography | 84.09 | 91.62 | 0.280 7 | 0.085 1 | 25.22 | 14.75 | 865 | 45 |
Sonar | 81.87 | 84.21 | 0.200 2 | 0.173 6 | 17.80 | 15.55 | 24 | 24 |
Spect | 86.96 | 94.77 | 0.297 8 | 0.220 5 | 6.00 | 5.00 | 52 | 81 |
Tic-tac-toe | 85.62 | 95.03 | 0.313 9 | 0.219 5 | 6.20 | 5.00 | 80 | 143 |
Vote | 93.51 | 95.03 | 0.303 6 | 0.219 5 | 8.60 | 5.00 | 80 | 85 |
Waveform | 95.02 | 96.89 | 0.236 7 | 0.221 4 | 6.50 | 6.30 | 18 | 17 |
Wine | 96.14 | 97.53 | 0.300 8 | 0.261 5 | 7.95 | 7.20 | 21 | 21 |
Zoo | 95.72 | 97.14 | 0.240 2 | 0.227 9 | 7.10 | 6.55 | 18 | 16 |
Tab. 8 Experimental results of FSNM and FSNB on classification accuracy, fitness value and number of selected features
数据集 | 分类精度/% | 适应度值 | 所选特征数 | 运行时间/s | ||||
---|---|---|---|---|---|---|---|---|
FSNM | FSNB | FSNM | FSNB | FSNM | FSNB | FSNM | FSNB | |
Average | 87.47 | 91.51 | 0.274 8 | 0.231 3 | 10.45 | 8.64 | 251 | 198 |
Breastcancer | 95.24 | 95.67 | 0.370 8 | 0.342 8 | 6.25 | 5.75 | 119 | 118 |
Congress | 72.68 | 77.02 | 0.208 9 | 0.209 0 | 5.00 | 5.05 | 55 | 55 |
Heart | 75.22 | 76.98 | 0.401 9 | 0.408 3 | 8.15 | 9.15 | 33 | 33 |
Hepatitis | 94.64 | 95.04 | 0.277 5 | 0.272 0 | 15.40 | 14.65 | 1 075 | 1 073 |
Ionosphere | 94.10 | 95.04 | 0.276 1 | 0.272 0 | 15.10 | 14.65 | 1 074 | 1 065 |
Lungcancer | 73.77 | 89.15 | 0.138 6 | 0.105 0 | 11.00 | 6.30 | 6 | 5 |
Lymphography | 84.09 | 91.62 | 0.280 7 | 0.085 1 | 25.22 | 14.75 | 865 | 45 |
Sonar | 81.87 | 84.21 | 0.200 2 | 0.173 6 | 17.80 | 15.55 | 24 | 24 |
Spect | 86.96 | 94.77 | 0.297 8 | 0.220 5 | 6.00 | 5.00 | 52 | 81 |
Tic-tac-toe | 85.62 | 95.03 | 0.313 9 | 0.219 5 | 6.20 | 5.00 | 80 | 143 |
Vote | 93.51 | 95.03 | 0.303 6 | 0.219 5 | 8.60 | 5.00 | 80 | 85 |
Waveform | 95.02 | 96.89 | 0.236 7 | 0.221 4 | 6.50 | 6.30 | 18 | 17 |
Wine | 96.14 | 97.53 | 0.300 8 | 0.261 5 | 7.95 | 7.20 | 21 | 21 |
Zoo | 95.72 | 97.14 | 0.240 2 | 0.227 9 | 7.10 | 6.55 | 18 | 16 |
数据集 | FSNB | GANRS | PSONRS | ABCNRS | FANRS | SSONRS | CSNRS | HSNRS | IRRANRS |
---|---|---|---|---|---|---|---|---|---|
Average | 89.70 | 79.24 | 83.34 | 85.92 | 81.44 | 86.23 | 85.42 | 86.16 | 88.57 |
Breastcancer | 95.67 | 95.18 | 96.93 | 97.81 | 93.42 | 96.49 | 95.61 | 97.37 | 98.68 |
Congress | 77.02 | 90.43 | 97.24 | 95.86 | 94.48 | 96.93 | 94.48 | 95.17 | 96.93 |
Heart | 76.98 | 66.52 | 69.10 | 71.54 | 70.86 | 72.91 | 71.61 | 75.00 | 76.80 |
Ionosphere | 95.04 | 80.58 | 81.39 | 86.02 | 81.98 | 85.60 | 86.27 | 85.49 | 86.61 |
Lungcancer | 89.15 | 62.86 | 68.60 | 70.22 | 65.37 | 71.56 | 72.59 | 69.26 | 79.26 |
Waveform | 96.89 | 78.92 | 80.88 | 83.04 | 79.42 | 83.18 | 82.96 | 84.99 | 84.64 |
Zoo | 97.14 | 80.21 | 89.22 | 96.97 | 84.58 | 96.97 | 94.44 | 95.87 | 97.06 |
Tab. 9 Experimental results of FSNB and eight feature selection algorithms combining NRS and optimization algorithms on classification accuracy
数据集 | FSNB | GANRS | PSONRS | ABCNRS | FANRS | SSONRS | CSNRS | HSNRS | IRRANRS |
---|---|---|---|---|---|---|---|---|---|
Average | 89.70 | 79.24 | 83.34 | 85.92 | 81.44 | 86.23 | 85.42 | 86.16 | 88.57 |
Breastcancer | 95.67 | 95.18 | 96.93 | 97.81 | 93.42 | 96.49 | 95.61 | 97.37 | 98.68 |
Congress | 77.02 | 90.43 | 97.24 | 95.86 | 94.48 | 96.93 | 94.48 | 95.17 | 96.93 |
Heart | 76.98 | 66.52 | 69.10 | 71.54 | 70.86 | 72.91 | 71.61 | 75.00 | 76.80 |
Ionosphere | 95.04 | 80.58 | 81.39 | 86.02 | 81.98 | 85.60 | 86.27 | 85.49 | 86.61 |
Lungcancer | 89.15 | 62.86 | 68.60 | 70.22 | 65.37 | 71.56 | 72.59 | 69.26 | 79.26 |
Waveform | 96.89 | 78.92 | 80.88 | 83.04 | 79.42 | 83.18 | 82.96 | 84.99 | 84.64 |
Zoo | 97.14 | 80.21 | 89.22 | 96.97 | 84.58 | 96.97 | 94.44 | 95.87 | 97.06 |
数据集 | FSNB | GANRS | PSONRS | ABCNRS | FANRS | SSONRS | CSNRS | HSNRS | IRRANRS |
---|---|---|---|---|---|---|---|---|---|
Average | 7.68 | 685.39 | 576.52 | 608.29 | 583.31 | 579.51 | 644.16 | 563.34 | 354.53 |
Breastcancer | 5.75 | 466.23 | 381.79 | 359.99 | 370.82 | 355.51 | 389.90 | 399.55 | 312.87 |
Congress | 5.05 | 245.21 | 206.58 | 136.81 | 128.19 | 140.33 | 124.85 | 134.07 | 124.67 |
Heart | 9.15 | 124.71 | 119.53 | 110.70 | 105.30 | 108.86 | 122.15 | 112.48 | 106.22 |
Ionosphere | 14.65 | 183.68 | 179.43 | 124.08 | 137.45 | 133.20 | 123.48 | 134.96 | 125.27 |
Lungcancer | 6.30 | 16.39 | 13.10 | 13.19 | 11.82 | 10.81 | 10.63 | 10.81 | 11.09 |
Waveform | 6.30 | 3 702.00 | 3 085.00 | 3 478.50 | 3 289.50 | 3 276.00 | 3 692.00 | 3 112.50 | 1 773.00 |
Zoo | 6.55 | 59.52 | 50.20 | 34.78 | 40.05 | 31.83 | 46.11 | 39.04 | 28.60 |
Tab. 10 Experimental results of FSNB and eight feature selection algorithms combining NRS and optimization algorithms on number of selected features
数据集 | FSNB | GANRS | PSONRS | ABCNRS | FANRS | SSONRS | CSNRS | HSNRS | IRRANRS |
---|---|---|---|---|---|---|---|---|---|
Average | 7.68 | 685.39 | 576.52 | 608.29 | 583.31 | 579.51 | 644.16 | 563.34 | 354.53 |
Breastcancer | 5.75 | 466.23 | 381.79 | 359.99 | 370.82 | 355.51 | 389.90 | 399.55 | 312.87 |
Congress | 5.05 | 245.21 | 206.58 | 136.81 | 128.19 | 140.33 | 124.85 | 134.07 | 124.67 |
Heart | 9.15 | 124.71 | 119.53 | 110.70 | 105.30 | 108.86 | 122.15 | 112.48 | 106.22 |
Ionosphere | 14.65 | 183.68 | 179.43 | 124.08 | 137.45 | 133.20 | 123.48 | 134.96 | 125.27 |
Lungcancer | 6.30 | 16.39 | 13.10 | 13.19 | 11.82 | 10.81 | 10.63 | 10.81 | 11.09 |
Waveform | 6.30 | 3 702.00 | 3 085.00 | 3 478.50 | 3 289.50 | 3 276.00 | 3 692.00 | 3 112.50 | 1 773.00 |
Zoo | 6.55 | 59.52 | 50.20 | 34.78 | 40.05 | 31.83 | 46.11 | 39.04 | 28.60 |
数据集 | FSNB | BGWO | ABGWO | ABGWO-V1 | ABGWO-V2 | ABGWO-V3 | ABGWO-V4 |
---|---|---|---|---|---|---|---|
Average | 91.93 | 77.61 | 78.39 | 78.29 | 78.04 | 78.08 | 78.14 |
Breastcancer | 95.67 | 80.48 | 80.58 | 80.42 | 80.24 | 80.59 | 79.96 |
Congress | 77.02 | 83.45 | 84.02 | 83.32 | 83.18 | 83.19 | 83.57 |
Ionosphere | 95.04 | 85.95 | 86.90 | 86.90 | 86.91 | 86.63 | 86.37 |
Lymphography | 91.62 | 46.24 | 47.27 | 47.27 | 47.33 | 47.14 | 47.55 |
Sonar | 84.21 | 80.22 | 81.23 | 81.23 | 81.73 | 81.57 | 81.17 |
Spect | 94.77 | 73.31 | 74.69 | 74.69 | 73.95 | 74.06 | 73.71 |
Tic-tac-toe | 95.03 | 77.46 | 76.15 | 76.15 | 75.66 | 75.75 | 76.71 |
Waveform | 96.89 | 81.71 | 82.38 | 82.38 | 81.56 | 81.74 | 81.79 |
Zoo | 97.14 | 89.69 | 92.28 | 92.28 | 91.81 | 92.09 | 92.39 |
Tab. 11 Experimental results of FSNB and six feature selection algorithms based on binary grey wolf optimizer on classification accuracy
数据集 | FSNB | BGWO | ABGWO | ABGWO-V1 | ABGWO-V2 | ABGWO-V3 | ABGWO-V4 |
---|---|---|---|---|---|---|---|
Average | 91.93 | 77.61 | 78.39 | 78.29 | 78.04 | 78.08 | 78.14 |
Breastcancer | 95.67 | 80.48 | 80.58 | 80.42 | 80.24 | 80.59 | 79.96 |
Congress | 77.02 | 83.45 | 84.02 | 83.32 | 83.18 | 83.19 | 83.57 |
Ionosphere | 95.04 | 85.95 | 86.90 | 86.90 | 86.91 | 86.63 | 86.37 |
Lymphography | 91.62 | 46.24 | 47.27 | 47.27 | 47.33 | 47.14 | 47.55 |
Sonar | 84.21 | 80.22 | 81.23 | 81.23 | 81.73 | 81.57 | 81.17 |
Spect | 94.77 | 73.31 | 74.69 | 74.69 | 73.95 | 74.06 | 73.71 |
Tic-tac-toe | 95.03 | 77.46 | 76.15 | 76.15 | 75.66 | 75.75 | 76.71 |
Waveform | 96.89 | 81.71 | 82.38 | 82.38 | 81.56 | 81.74 | 81.79 |
Zoo | 97.14 | 89.69 | 92.28 | 92.28 | 91.81 | 92.09 | 92.39 |
数据集 | FSNB | BGWO | ABGWO | ABGWO-V1 | ABGWO-V2 | ABGWO-V3 | ABGWO-V4 |
---|---|---|---|---|---|---|---|
Average | 8.73 | 16.62 | 13.19 | 12.78 | 12.53 | 12.14 | 12.86 |
Breastcancer | 5.75 | 5.30 | 3.80 | 4.45 | 3.75 | 3.95 | 4.05 |
Congress | 5.05 | 9.90 | 8.25 | 7.75 | 8.10 | 7.40 | 8.50 |
Ionosphere | 14.65 | 21.00 | 16.75 | 15.10 | 14.00 | 14.90 | 15.75 |
Lymphography | 14.75 | 11.85 | 8.85 | 8.65 | 8.00 | 7.65 | 9.00 |
Sonar | 15.55 | 36.60 | 30.45 | 28.20 | 28.65 | 27.25 | 27.85 |
Spect | 5.00 | 15.45 | 11.15 | 11.65 | 11.85 | 10.65 | 11.60 |
Tic-tac-toe | 5.00 | 6.80 | 5.90 | 6.00 | 5.30 | 5.35 | 5.85 |
Waveform | 6.30 | 31.30 | 24.35 | 23.75 | 24.55 | 23.05 | 24.10 |
Zoo | 6.55 | 11.40 | 9.20 | 9.45 | 8.60 | 9.10 | 9.05 |
Tab. 12 Experimental results of FSNB and six feature selection algorithms based on binary grey wolf optimizer on number of selected features
数据集 | FSNB | BGWO | ABGWO | ABGWO-V1 | ABGWO-V2 | ABGWO-V3 | ABGWO-V4 |
---|---|---|---|---|---|---|---|
Average | 8.73 | 16.62 | 13.19 | 12.78 | 12.53 | 12.14 | 12.86 |
Breastcancer | 5.75 | 5.30 | 3.80 | 4.45 | 3.75 | 3.95 | 4.05 |
Congress | 5.05 | 9.90 | 8.25 | 7.75 | 8.10 | 7.40 | 8.50 |
Ionosphere | 14.65 | 21.00 | 16.75 | 15.10 | 14.00 | 14.90 | 15.75 |
Lymphography | 14.75 | 11.85 | 8.85 | 8.65 | 8.00 | 7.65 | 9.00 |
Sonar | 15.55 | 36.60 | 30.45 | 28.20 | 28.65 | 27.25 | 27.85 |
Spect | 5.00 | 15.45 | 11.15 | 11.65 | 11.85 | 10.65 | 11.60 |
Tic-tac-toe | 5.00 | 6.80 | 5.90 | 6.00 | 5.30 | 5.35 | 5.85 |
Waveform | 6.30 | 31.30 | 24.35 | 23.75 | 24.55 | 23.05 | 24.10 |
Zoo | 6.55 | 11.40 | 9.20 | 9.45 | 8.60 | 9.10 | 9.05 |
平均排序 | |||||||
---|---|---|---|---|---|---|---|
FSNB | ARRSFA | PSORSFS | FSARSR | QCSIA_FS | DQBGOA_MR | ||
1.50 | 4.20 | 4.60 | 4.35 | 4.25 | 2.10 | 25.56 | 9.41 |
Tab. 13 Statistical test of FSNB and five optimized feature selection algorithms based on rough set
平均排序 | |||||||
---|---|---|---|---|---|---|---|
FSNB | ARRSFA | PSORSFS | FSARSR | QCSIA_FS | DQBGOA_MR | ||
1.50 | 4.20 | 4.60 | 4.35 | 4.25 | 2.10 | 25.56 | 9.41 |
平均排序 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
FSNB | GARS | PSORS | ABCRS | FARS | SSORS | CSRS | HSRS | IRRARS | ||
2.57 | 8.86 | 7.71 | 4.29 | 6.93 | 4.07 | 4.14 | 4.57 | 1.86 | 41.09 | 16.54 |
Tab. 14 Statistical test of FSNB and eight feature selection algorithms combining rough set and optimization algorithms
平均排序 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
FSNB | GARS | PSORS | ABCRS | FARS | SSORS | CSRS | HSRS | IRRARS | ||
2.57 | 8.86 | 7.71 | 4.29 | 6.93 | 4.07 | 4.14 | 4.57 | 1.86 | 41.09 | 16.54 |
平均排序 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
FSNB | GANRS | PSONRS | ABCNRS | FANRS | SSONRS | CSNRS | HSNRS | IRRANRS | ||
2.86 | 8.71 | 6.00 | 4.21 | 7.64 | 3.93 | 5.29 | 4.29 | 2.07 | 34.77 | 9.83 |
Tab. 15 Statistical test of FSNB and eight feature selection algorithms combining NRS and optimization algorithms
平均排序 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
FSNB | GANRS | PSONRS | ABCNRS | FANRS | SSONRS | CSNRS | HSNRS | IRRANRS | ||
2.86 | 8.71 | 6.00 | 4.21 | 7.64 | 3.93 | 5.29 | 4.29 | 2.07 | 34.77 | 9.83 |
平均排序 | ||||||||
---|---|---|---|---|---|---|---|---|
FSNB | BGWO | ABGWO | ABGWO-V1 | ABGWO-V2 | ABGWO-V3 | ABGWO-V4 | ||
1.67 | 5.56 | 3.28 | 3.83 | 4.78 | 4.67 | 4.22 | 18.51 | 4.17 |
Tab. 16 Statistical test of FSNB and six feature selection algorithms based on binary grey wolf optimizer
平均排序 | ||||||||
---|---|---|---|---|---|---|---|---|
FSNB | BGWO | ABGWO | ABGWO-V1 | ABGWO-V2 | ABGWO-V3 | ABGWO-V4 | ||
1.67 | 5.56 | 3.28 | 3.83 | 4.78 | 4.67 | 4.22 | 18.51 | 4.17 |
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