Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (5): 1408-1414.DOI: 10.11772/j.issn.1001-9081.2023121829
Special Issue: 进化计算专题(2024年第5期“进化计算专题”导读,全文已上线)
• Special issue on evolutionary calculation • Previous Articles Next Articles
Lin GAO1, Yu ZHOU1(), Tak Wu KWONG2
Received:
2024-01-01
Accepted:
2024-01-19
Online:
2024-04-26
Published:
2024-05-10
Contact:
Yu ZHOU
About author:
GAO Lin, born in 1995, M. S. candidate. His research interests include feature selection, multi-objective optimization.Supported by:
通讯作者:
周宇
作者简介:
高麟(1995—),男,湖南常德人,硕士研究生,主要研究方向:特征选择、多目标优化基金资助:
CLC Number:
Lin GAO, Yu ZHOU, Tak Wu KWONG. Evolutionary bi-level adaptive local feature selection[J]. Journal of Computer Applications, 2024, 44(5): 1408-1414.
高麟, 周宇, 邝得互. 进化双层自适应局部特征选择[J]. 《计算机应用》唯一官方网站, 2024, 44(5): 1408-1414.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023121829
数据集 | 实例数 | 特征数 | 类别数 |
---|---|---|---|
Iris | 150 | 4 | 3 |
Wine | 178 | 13 | 3 |
Breast Cancer | 699 | 9 | 2 |
Diabetes | 768 | 8 | 2 |
Heart Statlog | 270 | 13 | 2 |
Vehicle | 846 | 18 | 4 |
Ionosphere | 351 | 34 | 2 |
Sonar | 208 | 60 | 2 |
Musk1 | 476 | 166 | 2 |
Yale | 165 | 1 024 | 40 |
ORL | 400 | 1 024 | 40 |
Tab. 1 Details of UCI datasets
数据集 | 实例数 | 特征数 | 类别数 |
---|---|---|---|
Iris | 150 | 4 | 3 |
Wine | 178 | 13 | 3 |
Breast Cancer | 699 | 9 | 2 |
Diabetes | 768 | 8 | 2 |
Heart Statlog | 270 | 13 | 2 |
Vehicle | 846 | 18 | 4 |
Ionosphere | 351 | 34 | 2 |
Sonar | 208 | 60 | 2 |
Musk1 | 476 | 166 | 2 |
Yale | 165 | 1 024 | 40 |
ORL | 400 | 1 024 | 40 |
算法 | 参数设置 |
---|---|
LFSDC | |
LFS-MOBPSO | 网格扩张系数=0.1,网格划分数=30 |
RP-NSGA,BiLFS | 每代交叉次数=20,每代变异次数=20, 变异概率 |
Tab. 2 Specific parameter settings of various LFS algorithms
算法 | 参数设置 |
---|---|
LFSDC | |
LFS-MOBPSO | 网格扩张系数=0.1,网格划分数=30 |
RP-NSGA,BiLFS | 每代交叉次数=20,每代变异次数=20, 变异概率 |
数据集 | 算法 | (ACC ± STD)/% | 用时/s | 数据集 | 算法 | (ACC ± STD)/% | 用时/s |
---|---|---|---|---|---|---|---|
Iris | BiLFS | 96.67 ± 0.71 | 17.70 | Diabetes | BiLFS | 75.66 ± 0.63 | 2 032.53 |
LFS-MOBPSO | 97.13 ± 0.38 | 617.24 | LFS-MOBPSO | 76.95 ± 0.57 | 39 642.42 | ||
RP-NSGA | 97.27 ± 0.68 | 706.33 | RP-NSGA | 75.96 ± 0.69 | 42 348.12 | ||
Wine | BiLFS | 97.18 ± 0.38 | 39.01 | Heart Statlog | BiLFS | 79.10 ± 0.61 | 66.03 |
LFS-MOBPSO | 97.16 ± 0.37 | 985.33 | LFS-MOBPSO | 85.19 ± 0.55 | 2 004.14 | ||
RP-NSGA | 97.98 ± 0.31 | 1 231.01 | RP-NSGA | 85.11 ± 0.69 | 2 024.32 | ||
Vehicle | BiLFS | 71.16 ± 1.36 | 265.07 | Sonar | BiLFS | 89.26 ± 0.83 | 102.66 |
LFS-MOBPSO | 69.83 ± 1.81 | 42 081.17 | LFS-MOBPSO | 88.98 ± 0.65 | 786.05 | ||
RP-NSGA | 70.76 ± 1.67 | 59 446.89 | RP-NSGA | 89.39 ± 0.68 | 875.24 | ||
Breast Cancer | BiLFS | 95.61 ± 0.66 | 1 223.01 | ORL | BiLFS | 97.00 ± 0.56 | 5 942.10 |
LFS-MOBPSO | 96.86 ± 0.47 | 52 143.74 | LFS-MOBPSO | 96.65 ± 0.51 | 30 421.14 | ||
RP-NSGA | 96.42 ± 0.58 | 60 845.65 | RP-NSGA | 96.75 ± 0.42 | 31 247.68 | ||
Ionosphere | BiLFS | 87.43 ± 0.34 | 210.79 | Yale | BiLFS | 81.83 ± 0.33 | 952.79 |
LFS-MOBPSO | 90.84 ± 0.24 | 4 015.23 | LFS-MOBPSO | 86.07 ± 0.29 | 1 984.14 | ||
RP-NSGA | 93.91 ± 0.23 | 4 323.65 | RP-NSGA | 88.92 ± 0.31 | 2 158.30 | ||
Musk1 | BiLFS | 91.00 ± 0.87 | 1 114.36 | ||||
LFS-MOBPSO | 91.06 ± 0.63 | 10 125.53 | |||||
RP-NSGA | 91.11 ± 0.70 | 12 842.36 |
Tab. 3 Comparison results with LFS methods based on evolutionary algorithms
数据集 | 算法 | (ACC ± STD)/% | 用时/s | 数据集 | 算法 | (ACC ± STD)/% | 用时/s |
---|---|---|---|---|---|---|---|
Iris | BiLFS | 96.67 ± 0.71 | 17.70 | Diabetes | BiLFS | 75.66 ± 0.63 | 2 032.53 |
LFS-MOBPSO | 97.13 ± 0.38 | 617.24 | LFS-MOBPSO | 76.95 ± 0.57 | 39 642.42 | ||
RP-NSGA | 97.27 ± 0.68 | 706.33 | RP-NSGA | 75.96 ± 0.69 | 42 348.12 | ||
Wine | BiLFS | 97.18 ± 0.38 | 39.01 | Heart Statlog | BiLFS | 79.10 ± 0.61 | 66.03 |
LFS-MOBPSO | 97.16 ± 0.37 | 985.33 | LFS-MOBPSO | 85.19 ± 0.55 | 2 004.14 | ||
RP-NSGA | 97.98 ± 0.31 | 1 231.01 | RP-NSGA | 85.11 ± 0.69 | 2 024.32 | ||
Vehicle | BiLFS | 71.16 ± 1.36 | 265.07 | Sonar | BiLFS | 89.26 ± 0.83 | 102.66 |
LFS-MOBPSO | 69.83 ± 1.81 | 42 081.17 | LFS-MOBPSO | 88.98 ± 0.65 | 786.05 | ||
RP-NSGA | 70.76 ± 1.67 | 59 446.89 | RP-NSGA | 89.39 ± 0.68 | 875.24 | ||
Breast Cancer | BiLFS | 95.61 ± 0.66 | 1 223.01 | ORL | BiLFS | 97.00 ± 0.56 | 5 942.10 |
LFS-MOBPSO | 96.86 ± 0.47 | 52 143.74 | LFS-MOBPSO | 96.65 ± 0.51 | 30 421.14 | ||
RP-NSGA | 96.42 ± 0.58 | 60 845.65 | RP-NSGA | 96.75 ± 0.42 | 31 247.68 | ||
Ionosphere | BiLFS | 87.43 ± 0.34 | 210.79 | Yale | BiLFS | 81.83 ± 0.33 | 952.79 |
LFS-MOBPSO | 90.84 ± 0.24 | 4 015.23 | LFS-MOBPSO | 86.07 ± 0.29 | 1 984.14 | ||
RP-NSGA | 93.91 ± 0.23 | 4 323.65 | RP-NSGA | 88.92 ± 0.31 | 2 158.30 | ||
Musk1 | BiLFS | 91.00 ± 0.87 | 1 114.36 | ||||
LFS-MOBPSO | 91.06 ± 0.63 | 10 125.53 | |||||
RP-NSGA | 91.11 ± 0.70 | 12 842.36 |
数据集 | 算法 | (ACC ± STD)/% | 用时/s | 数据集 | 算法 | (ACC ± STD)/% | 用时/s |
---|---|---|---|---|---|---|---|
Iris | BiLFS | 96.67 ± 0.71 | 17.70 | Diabetes | BiLFS | 75.66 ± 0.63 | 2 032.53 |
LFSDC | 94.67 ± 0.42 | 35.12 | LFSDC | 75.39 ± 0.56 | 324.40 | ||
Wine | BiLFS | 97.18 ± 0.38 | 39.01 | Heart Statlog | BiLFS | 79.10 ± 0.61 | 66.00 |
LFSDC | 96.84 ± 0.39 | 59.52 | LFSDC | 83.24 ± 0.60 | 84.50 | ||
Vehicle | BiLFS | 71.16 ± 1.36 | 265.07 | Sonar | BiLFS | 89.26 ± 0.83 | 102.66 |
LFSDC | 69.02 ± 0.90 | 230.04 | LFSDC | 85.30 ± 0.79 | 51.52 | ||
Breast Cancer | BiLFS | 95.61 ± 0.66 | 1 223.01 | ORL | BiLFS | 97.00 ± 0.56 | 5 942.10 |
LFSDC | 96.28 ± 0.38 | 980.00 | LFSDC | 96.65 ± 0.34 | 1 423.21 | ||
Ionosphere | BiLFS | 87.43 ± 0.34 | 210.79 | Yale | BiLFS | 81.83 ± 0.33 | 952.79 |
LFSDC | 91.79 ± 0.40 | 170.00 | LFSDC | 81.73 ± 0.29 | 413.65 | ||
Musk1 | BiLFS | 91.00 ± 0.87 | 1 114.36 | ||||
LFSDC | 87.60 ± 0.88 | 420.00 |
Tab. 4 Comparison results with LFSDC algorithm
数据集 | 算法 | (ACC ± STD)/% | 用时/s | 数据集 | 算法 | (ACC ± STD)/% | 用时/s |
---|---|---|---|---|---|---|---|
Iris | BiLFS | 96.67 ± 0.71 | 17.70 | Diabetes | BiLFS | 75.66 ± 0.63 | 2 032.53 |
LFSDC | 94.67 ± 0.42 | 35.12 | LFSDC | 75.39 ± 0.56 | 324.40 | ||
Wine | BiLFS | 97.18 ± 0.38 | 39.01 | Heart Statlog | BiLFS | 79.10 ± 0.61 | 66.00 |
LFSDC | 96.84 ± 0.39 | 59.52 | LFSDC | 83.24 ± 0.60 | 84.50 | ||
Vehicle | BiLFS | 71.16 ± 1.36 | 265.07 | Sonar | BiLFS | 89.26 ± 0.83 | 102.66 |
LFSDC | 69.02 ± 0.90 | 230.04 | LFSDC | 85.30 ± 0.79 | 51.52 | ||
Breast Cancer | BiLFS | 95.61 ± 0.66 | 1 223.01 | ORL | BiLFS | 97.00 ± 0.56 | 5 942.10 |
LFSDC | 96.28 ± 0.38 | 980.00 | LFSDC | 96.65 ± 0.34 | 1 423.21 | ||
Ionosphere | BiLFS | 87.43 ± 0.34 | 210.79 | Yale | BiLFS | 81.83 ± 0.33 | 952.79 |
LFSDC | 91.79 ± 0.40 | 170.00 | LFSDC | 81.73 ± 0.29 | 413.65 | ||
Musk1 | BiLFS | 91.00 ± 0.87 | 1 114.36 | ||||
LFSDC | 87.60 ± 0.88 | 420.00 |
1 | 李郅琴,杜建强,聂斌,等.特征选择方法综述[J].计算机工程与应用,2019,55(24):10-19. 10.3778/j.issn.1002-8331.1909-0066 |
LI Z Q, DU J Q, NIE B, et al. Summary of feature selection methods[J]. Computer Engineering and Applications, 2019, 55(24): 10-19. 10.3778/j.issn.1002-8331.1909-0066 | |
2 | UYSAL A K. An improved global feature selection scheme for text classification[J]. Expert Systems with Applications, 2016, 43: 82-92. 10.1016/j.eswa.2015.08.050 |
3 | DUDEK G. An artificial immune system for classification with local feature selection[J]. IEEE Transactions on Evolutionary Computation, 2012, 16(6): 847-860. 10.1109/tevc.2011.2173580 |
4 | ARMANFARD N, REILLY J P, KOMEILI M. Local feature selection for data classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 38(6): 1217-1227. 10.1109/tpami.2015.2478471 |
5 | WANG Y, LI T. Local feature selection based on artificial immune system for classification[J]. Applied Soft Computing, 2020, 87: 105989. 10.1016/j.asoc.2019.105989 |
6 | ARMANFARD N, REILLY J P, KOMEILI M. Logistic localized modeling of the sample space for feature selection and classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 29(5): 1396-1413. 10.1109/tnnls.2017.2676101 |
7 | ZHOU Y, QIU Y, KWONG S. Region purity-based local feature selection: a multi-objective perspective[J]. IEEE Transactions on Evolutionary Computation, 2023, 27(4): 787-801. 10.1109/tevc.2022.3222297 |
8 | 姬强,孙艳丰,胡永利,等.深度聚类算法研究综述[J].北京工业大学学报,2021,47(8):912-924. 10.11936/bjutxb2021010013 |
JI Q, SUN Y F, HU Y L, et al. Review of clustering with deep learning[J]. Journal of Beijing University of Technology, 2021, 47(8): 912-924. 10.11936/bjutxb2021010013 | |
9 | 孙林,刘梦含,徐久成.基于优化初始聚类中心和轮廓系数的K‑means聚类算法[J].模糊系统与数学,2022,36(1):47-65. |
SUN L, LIU M H, XU J C. K-means clustering algorithm using optimal initial clustering center and contour coefficient[J]. Fuzzy Systems and Mathematics, 2013, 36(1): 47-65. | |
10 | SINHA A, MALO P, DEB K. A review on bilevel optimization: from classical to evolutionary approaches and applications[J]. IEEE Transactions on Evolutionary Computation, 2017, 22(2): 276-295. 10.1109/tevc.2017.2712906 |
11 | 赵波,汪湘晋,张雪松,等.考虑需求侧响应及不确定性的微电网双层优化配置方法[J].电工技术学报,2018,33(14):3284-3295. 10.19595/j.cnki.1000-6753.tces.170388 |
ZHAO B, WANG X J, ZHANG X S, et al. Two-layer method of microgrid optimal sizing considering demand-side response and uncertainties[J]. Transactions of China Electrotechnical Society, 2018, 33(14): 3284-3295. 10.19595/j.cnki.1000-6753.tces.170388 | |
12 | 米阳,李战强,吴彦伟,等.基于两级需求响应的并网微电网双层优化调度[J].电网技术,2018,42(6):1899-1906. 10.13335/j.1000-3673.pst.2017.2435 |
MI Y, LI Z Q, WU Y W, et al. Bi-layer optimal dispatch of grid-connected microgrid based on two-stage demand response[J]. Power System Technology, 2018,42(6):1899-1906. 10.13335/j.1000-3673.pst.2017.2435 | |
13 | 林晓明,张勇军,陈伯达,等.计及多评价指标的园区能源互联网双层优化配置[J].电力系统自动化,2019,43(20):8-15. 10.7500/AEPS20180811006 |
LIN X M, ZHANG Y J, CHEN B D, et al. Bi-level optimal configuration of park energy internet considering multiple evaluation indicators[J]. Automation of Electric Power Systems,2019,43(20):8-15. 10.7500/AEPS20180811006 | |
14 | DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA‑Ⅱ[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197. 10.1109/4235.996017 |
15 | ZHOU Y, GAO L, WANG D, et al. Imbalanced multi-fault diagnosis via improved localized feature selection[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 3531611. 10.1109/tim.2023.3317923 |
16 | KOHAVI R. A study of cross-validation and bootstrap for accuracy estimation and model selection[C]// Proceedings of the 14th International Joint Conference on Artificial Intelligence: Volume 2. San Francisco, CA: Morgan Kaufmann Publishers Inc., 1995: 1137-1145. |
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