Systems Engineering and Electronics ›› 2020, Vol. 42 ›› Issue (11): 2654-2660.doi: 10.3969/j.issn.1001-506X.2020.11.30
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Ruifeng LI(), Aiqiang XU(
), Weichao SUN(
), Yangyong WU(
)
Received:
2020-02-07
Online:
2020-11-01
Published:
2020-11-05
Supported by:
CLC Number:
Ruifeng LI, Aiqiang XU, Weichao SUN, Yangyong WU. Preprocessing method based on sample resampling for imbalanced data of electronic circuits[J]. Systems Engineering and Electronics, 2020, 42(11): 2654-2660.
Table 3
Measured circuit data (part) V"
序号 | V1_max | V1_min | V2 | V3 | V4 | V5 | V6 | V7 | V8 | 属性 |
1 | -7.730 | -6.360 | -6.923 | -6.928 | -6.281 | -2.811 | -2.981 | -5.579 | -0.140 | 正常 |
2 | -7.794 | -6.337 | -6.953 | -6.955 | -6.297 | -2.781 | -2.969 | -5.603 | -0.134 | |
⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | |
188 | -7.706 | -6.344 | -6.943 | -6.945 | -6.271 | -2.812 | -3.020 | -5.613 | -0.148 | |
189 | -7.760 | -6.622 | -7.106 | -7.089 | -6.533 | -2.656 | -2.456 | -4.548 | -0.133 | 故障 |
⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | |
233 | -7.792 | -6.597 | -7.078 | -7.049 | -6.503 | -2.670 | -2.544 | -4.726 | -0.113 |
Table 4
Performance comparison between F-value and G-mean"
数据集 | 算法 | F-value | G-mean | ||
均值 | 标准差 | 均值 | 标准差 | ||
Glass | 未处理 | 0.426 | 0.148 | 0.591 | 0.128 |
SMOTE | 0.479 | 0.153 | 0.573 | 0.126 | |
RU-SMOTE | 0.458 | 0.176 | 0.565 | 0.172 | |
BMS | 0.464 | 0.188 | 0.568 | 0.167 | |
RBCCS | 0.667 | 0.110 | 0.653 | 0.106 | |
Haberman | 未处理 | 0.336 | 0.108 | 0.497 | 0.094 |
SMOTE | 0.445 | 0.160 | 0.517 | 0.168 | |
RU-SMOTE | 0.432 | 0.187 | 0.536 | 0.184 | |
BMS | 0.474 | 0.155 | 0.568 | 0.135 | |
RBCCS | 0.639 | 0.081 | 0.633 | 0.069 | |
Ecoli | 未处理 | 0.344 | 0.102 | 0.492 | 0.084 |
SMOTE | 0.611 | 0.069 | 0.659 | 0.056 | |
RU-SMOTE | 0.603 | 0.125 | 0.671 | 0.117 | |
BMS | 0.430 | 0.118 | 0.546 | 0.097 | |
RBCCS | 0.643 | 0.051 | 0.632 | 0.046 | |
Breast | 未处理 | 0.333 | 0.088 | 0.517 | 0.075 |
SMOTE | 0.961 | 0.028 | 0.943 | 0.027 | |
RU-SMOTE | 0.925 | 0.072 | 0.931 | 0.069 | |
BMS | 0.918 | 0.041 | 0.938 | 0.033 | |
RBCCS | 0.950 | 0.022 | 0.950 | 0.021 | |
Serial Regulator | 未处理 | 0.498 | 0.166 | 0.693 | 0.129 |
SMOTE | 0.944 | 0.037 | 0.957 | 0.030 | |
RU-SMOTE | 0.902 | 0.104 | 0.946 | 0.097 | |
BMS | 0.931 | 0.039 | 0.952 | 0.028 | |
RBCCS | 0.953 | 0.028 | 0.963 | 0.026 |
1 | 景博, 黄以锋, 张建业. 航空电子系统故障预测与健康管理技术现状与发展[J]. 空军工程大学学报(自然科学版), 2010, 11 (6): 1- 6. |
JING B , HUANG Y F , ZHANG J Y . Status and perspectives of prognostics and health management technology of avionics system[J]. Journal of Air Force Engineering University (Natural Science Edition), 2010, 11 (6): 1- 6. | |
2 |
HENAO H , CAPOLINO G A , FERNANDEZ-CABANAS M , et al. Trends in fault diagnosis for electrical machines:a review of diagnostic techniques[J]. IEEE Industrial Electronics Magazine, 2014, 8 (2): 31- 42.
doi: 10.1109/MIE.2013.2287651 |
3 | GAO M , HONG X , CHEN S , et al. A combined SMOTE and PSO based RBF classifier for two class imbalanced problems[J]. Neuro Computing, 2011, 74 (17): 3456- 3466. |
4 | BELLINO A , FASANA A , GARIBALDI L , et al. PCA-based detection of damage in time-varying systems[J]. Mechanical Systems & Signal Processing, 2010, 24 (7): 2250- 2260. |
5 | SHIVAKUMAR K , KUMAR K S V P , SUMANTH K . An artificial immune system approach for the fault detection and diagnosis of a DC machine[J]. Journal of Medical Virology, 2014, 42 (4): 374- 379. |
6 |
NA S G , YANG I B , HEO H . Abnormality detection via SVDD technique of motor-generator system in HEV[J]. International Journal of Automotive Technology, 2014, 15 (4): 637- 643.
doi: 10.1007/s12239-014-0066-y |
7 | XIAO Y C , WANG H G , ZHANG L , et al. Two methods of selecting Gaussian kernel parameters for one-class SVM and their application to fault detection[J]. Knowledge Based Systems, 2014, 59 (3): 75- 84. |
8 | MIAO Z M, ZHAO L W, YUAN W W, et al. Multi-class imba-lanced learning implemented in network intrusion detection[C]//Proc.of the International Conference on Computer Science and Service System, 2011: 1395-1407. |
9 | SMAILOVIĆ J , GRČAR M , LAVRAČ N , et al. Stream-based active learning for sentiment analysis in the financial domain[J]. Information Sciences, 2014, 285 (C): 181- 203. |
10 | LIU Y Q, WANG C, ZHANG L. Decision tree based predictive models for breast cancer survivability on imbalanced data[C]//Proc.of the 3rd International Conference on Bioinformatics and Biomedical Engineering, 2009: 312-315. |
11 | 古平, 欧阳源遊. 基于混合采样的非平衡数据集分类研究[J]. 计算机应用研究, 2015, 32 (2): 379- 381. |
GU P , OUYANG Y Y . Classification research for unbalanced data based on mixed-sampling[J]. Applications Research of Computer, 2015, 32 (2): 379- 381. | |
12 |
YU H L , YANG X B , ZHENG S , et al. Active learning from imbalanced data: a solution of online weighted extreme learning machine[J]. IEEE Trans.on Neural Networks and Learning Systems, 2019, 30 (4): 1088- 1103.
doi: 10.1109/TNNLS.2018.2855446 |
13 |
CHEN S , HE H B , GARCIA E A . RAMOBoost: ranked minority oversampling in boosting[J]. IEEE Trans.on Neural Networks/a Publication of the IEEE Neural Networks Council, 2010, 21 (10): 1624- 1642.
doi: 10.1109/TNN.2010.2066988 |
14 | 蔡艳艳, 宋晓东. 针对非平衡数据分类的新型模糊SVM模型[J]. 西安电子科技大学学报, 2015, 42 (5): 134- 138, 174. |
CAI Y Y , SONG X D . A new fuzzy SVM model for imbalanced data classification[J]. Journal of Xidian University, 2015, 42 (5): 134- 138, 174. | |
15 | 王春玉, 苏宏业, 渠瑜, 等. 一种基于过抽样技术的非平衡数据集分类方法[J]. 计算机工程与应用, 2011, 47 (1): 139- 143. |
WANG C Y , SU H Y , QU Y , et al. Imbalanced data sets classification method based on over-sampling technique[J]. Computer Engineering and Applications, 2011, 47 (1): 139- 143. | |
16 | 张银峰, 郭华平, 职为梅, 等. 一种面向不平衡数据分类的组合剪枝方法[J]. 计算机工程, 2014, 40 (6): 157- 161, 165. |
ZHANG Y F , GUO H P , ZHI W M , et al. A classification method of combination pruning for unbalanced data[J]. Computer Engineering, 2014, 40 (6): 157- 161, 165. | |
17 | VONG C M , IP W F , WONG P K , et al. Predicting minority class for suspended particulate matters level by extreme lear-ning machine[J]. Neurocomputing, 2014, 128 (27): 136- 144. |
18 | CHAWLA N V , BOWYER K W , HALL L O , et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2011, 16 (1): 321- 357. |
19 | 刘余霞, 刘三民, 刘涛, 等. 一种新的过采样算法DB_SMOTE[J]. 计算机工程与应用, 2014, 50 (6): 92- 95. |
LIU Y X , LIU S M , LIU T , et al. A new algorithm of over-sampling DB_SMOTE[J]. Computer Engineering and Application, 2014, 50 (6): 92- 95. | |
20 |
YANG Y , LIU F , JIN Z Y , et al. Aliasing artefact suppression in compressed sensing MRI for random phase-encode undersampling[J]. IEEE Trans.on Biomedical Engineering, 2015, 62 (9): 2215- 2223.
doi: 10.1109/TBME.2015.2419372 |
21 |
JIA C Z , ZUO Y . S-SulfPred: a sensitive predictor to capture S-sulfenylation sites based on a resampling one-sided selection undersampling-synthetic minority oversampling technique[J]. Journal of Theoretical Biology, 2017, 422, 84- 89.
doi: 10.1016/j.jtbi.2017.03.031 |
22 | WILSON D L . Asymptotic properties of nearest neighbor rules using edited data[J]. IEEE Trans.on Systems Man & Cybernetics, 2007, SMC-2 (3): 408- 421. |
23 | 赵自翔, 王广亮, 李晓东. 基于支持向量机的不平衡数据分类的改进欠采样方法[J]. 中山大学学报(自然科学版), 2012, 51 (6): 15- 21. |
ZHAO Z X , WANG G L , LI X D . An improved under-sampling method based on support vector machine for imbalanced data classification[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2012, 51 (6): 15- 21. | |
24 | 冯宏伟, 姚博, 高原, 等. 基于边界混合采样的非均衡数据处理算法[J]. 控制与决策, 2017, 32 (10): 1831- 1836. |
FENG H W , YAO B , GAO Y , et al. Imbalanced data processing algorithm based on boundary mixed sampling[J]. Control and Decision, 2017, 32 (10): 1831- 1836. | |
25 |
GUSTAVO E A P A B , RONALDO C P , MARIA C M . A study of the behavior of several methods for balancing machine learning training data[J]. ACM SIGKDD Explorations Newslet-ter, 2004, 6 (1): 20- 29.
doi: 10.1145/1007730.1007735 |
26 | 谷琼, 袁磊, 宁彬, 等. 一种基于混合重取样策略的非平衡数据集分类算法[J]. 计算机工程与科学, 2012, 34 (10): 128- 134. |
GU Q , YUAN L , NING B , et al. A novel classification algorithm for imbalanced datasets based on hybrid resampling strategy[J]. Computer Engineering & Science, 2012, 34 (10): 128- 134. | |
27 | 陶新民, 郝思媛, 张冬雪, 等. 基于样本特性欠取样的不均衡支持向量机[J]. 控制与决策, 2013, 28 (7): 978- 984. |
TAO X M , HAO S Y , ZHANG D X , et al. Imbalanced support vector machines based on sample characteristics under-sampling[J]. Control and Decision, 2013, 28 (7): 978- 984. | |
28 |
SAVITHA R , SURESH S , SUNDARARAJAN N . Fast learning circular complex-valued extreme learning machine (CC-ELM) for real-valued classification problems[J]. Information Sciences, 2012, 187, 277- 290.
doi: 10.1016/j.ins.2011.11.003 |
29 |
HUANG G B , ZHOU H M , DING X J , et al. Extreme learning machine for regression and multiclass classification[J]. IEEE Trans.on Systems, Man, and Cybernetics, 2012, 42 (2): 513- 529.
doi: 10.1109/TSMCB.2011.2168604 |
30 | 薛丽香, 邱保志. 基于变异系数的边界点检测算法[J]. 模式识别与人工智能, 2009, 22 (5): 799- 802. |
XUE L X , QIU B Z . Boundary points detection algorithm based on coefficient of variation[J]. Pattern Recognition and Artificial Intelligence, 2009, 22 (5): 799- 802. | |
31 |
CRUZ R M O , SABOURIN R , CAVALCANTI G D C . META-DES Oracle: meta-learning and feature selection for dynamic ensemble selection[J]. Information Fusion, 2017, 38, 84- 103.
doi: 10.1016/j.inffus.2017.02.010 |
32 | PFAHRINGER B, BENSUSAN H, GIRAUD-CARRIER C G. Meta-learning by landmarking various learning algorithms[C]//Proc.of the 7th International Conference on Machine Learning, 2000: 743-750. |
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