计算机科学 ›› 2022, Vol. 49 ›› Issue (6): 134-141.doi: 10.11896/jsjkx.210500119
蔡欣雨, 冯翔, 虞慧群
CAI Xin-yu, FENG Xiang, YU Hui-qun
摘要： 进入智能化时代,需要在大数据平台上进行持续自主学习和优化,而持续自主学习的第一步就是进行数据增强。文中提出基于级联增强节点的宽度学习方法,为大数据平台上的持续自主学习提供了新的数据增强方法,也为后续在学习架构基础上的演化优化提供了可能。以时序预测问题为依托,但由于经典宽度学习是典型的前馈神经网络,并不适合建模动态时间序列,因此在传统的宽度学习系统中引入反馈结构,将增强节点层顺序连接,使得增强节点具有记忆性,能够保留部分历史信息。在进行特征提取时,采用了相空间重构来提取数据更本质的特征;同时,引入了权重因子,在训练时依据每个样本对模型的贡献度,为其独立分配不同的权重,从而消除噪声和离群点对学习过程的干扰,提高算法的预测准确率以及鲁棒性。实验结果表明所提算法是有效的。
中图分类号:
| [1] CAO B,WANG N,LI J,et al.Data Augmentation-Based Joint Learning for Heterogeneous Face Recognition[J].IEEE Tran-sactions on Neural Networks and Learning Systems,2018,30(6):1731-1743. [2] LEMLEY J,BAZRAFKAN S,CORCORAN P.Smart Augmentation Learning an Optimal Data Augmentation Strategy[J].IEEE Access,2017,5:5858-5869. [3] BOX G E P,PIERCE D A.Distribution of Residual Autocorrela-tions in Autoregressive-Integrated Moving Average Time Series Models[J].Journal of the American Statistical Association,1970,65(332):1509-1526. [4] YU G,HU J,ZHANG C,et al.Short-Term Traffic Flow Forecasting Based on Markov Chain Model[C]//IEEE IV2003 Intelligent Vehicles Symposium.IEEE,2003:208-212. [5] WANG J,DENG W,GUO Y.New Bayesian Combination Me-thod for Short-Term Traffic Flow Forecasting[J].Transportation Research Part C:Emerging Technologies,2014,43:79-94. [6] FENG L,ZHAO C,CHEN C L P,et al.BNGBS:An Efficient Network Boosting System With Triple Incremental Learning Capabilities for More Nodes,Samples,and Classes[J].Neurocomputing,2020,412:486-501. [7] HOU J X,LI Q,ZHU Y J,et al.Real-Time Forecasting System of PM2.5 Concentration Based on Spark Framework and Random Forest Model[J/OL].Science of Surveying and Mapping,2007.http://en.cnki.com.cn/Article_en/CJFDTotal-CHKD201701001.htm. [8] HOCHREITER S,SCHMIDHUBER J.Long Short-Term Me-mory[J].Neural Computation,1997,9(8):1735-1780. [9] XIE K,RONG Y T,HU F P,et al.Random Forest Algorithm Based on Data Integration[J].Computer Engineering,2020,46(12):290-298. [10] CHUNG J,GULCEHRE C,CHO K H,et al.Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling[J].arXiv:1412.3555,2014. [11] CHEN T T,LEE S J.A Weighted LS-SVM Based Learning System for Time Series Forecasting[J].Information Sciences,2015,299:99-116. [12] SONG X L,LIU Y Z,CHEN S F.Seasonal Time Series Forecasting Based on Seasonality Method Selection[J].Computer Engineering,2011,37(21):131-132,135. [13] HUANG S,WANG D,WU X,et al.Dsanet:Dual Self-attention Network for Multivariate Time Series Forecasting[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management.2019:2129-2132. [14] CHEN C L P.A Rapid Supervised Learning Neural Network for Function Interpolation and Approximation[J].IEEE Transactions on Neural Networks,1996,7(5):1220-1230. [15] CHEN C L P,LECLAIR S R,PAO Y H.An Incremental Adaptive Implementation of Functional-Link Processing for Function Approximation,Time-Series Prediction,and System Identification[J].Neurocomputing,1998,18(1/2/3):11-31. [16] CHEN C L P,WAN J Z.A Rapid Learning and Dynamic Stepwise Updating Algorithm for Flat Neural Networks and the Application to Time-Series Prediction[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B (Cybernetics),1999,29(1):62-72. [17] CHEN C L P,LIU Z.Broad Learning System:An Effective and Efficient Incremental Learning System without the Need for Deep Architecture[J].IEEE Transactions on Neural Networks and Learning Systems,2017,29(1):10-24. [18] HAN M,ZHANG R,QIU T,et al.Multivariate Chaotic Time Series Prediction Based on Improved Grey Relational Analysis[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2017,49(10):2144-2154. [19] WANG M D,XU X Y,YAN G W,et al.Ensemble Weighted Broad Learning System with AdaBoost for Imbalanced Classification[J].Computer Engineering,2020,48(4):99-105,112. [20] XU M,HAN M,CHEN C L P,et al.Recurrent Broad Learning Systems for Time Series Prediction[J].IEEE Transactions on Cybernetics,2018,50(4):1405-1417. |
| [1] | 王建明, 陈响育, 杨自忠, 史晨阳, 张宇航, 钱正坤. 不同数据增强方法对模型识别精度的影响 Influence of Different Data Augmentation Methods on Model Recognition Accuracy 计算机科学, 2022, 49(6A): 418-423. https://doi.org/10.11896/jsjkx.210700210 |
| [2] | 许华杰, 陈育, 杨洋, 秦远卓. 基于混合样本自动数据增强技术的半监督学习方法 Semi-supervised Learning Method Based on Automated Mixed Sample Data Augmentation Techniques 计算机科学, 2022, 49(3): 288-293. https://doi.org/10.11896/jsjkx.210100156 |
| [3] | 线岩团, 高凡雅, 相艳, 余正涛, 王剑. 融合多策略数据增强的低资源依存句法分析方法 Improving Low-resource Dependency Parsing Using Multi-strategy Data Augmentation 计算机科学, 2022, 49(1): 73-79. https://doi.org/10.11896/jsjkx.210900036 |
| [4] | 暴雨轩, 芦天亮, 杜彦辉, 石达. 基于i_ResNet34模型和数据增强的深度伪造视频检测方法 Deepfake Videos Detection Method Based on i_ResNet34 Model and Data Augmentation 计算机科学, 2021, 48(7): 77-85. https://doi.org/10.11896/jsjkx.210300258 |
| [5] | 刘舒康, 唐鹏, 金炜东. 基于智能数据增强和改进YOLOv3算法的接触网吊弦及支架检测研究 Study on Catenary Dropper and Support Detection Based on Intelligent Data Augmentation and Improved YOLOv3 计算机科学, 2020, 47(11A): 178-182. https://doi.org/10.11896/jsjkx.200200053 |
| [6] | 王绪亮, 聂铁铮, 唐欣然, 黄菊, 李迪, 闫铭森, 刘畅. 流式数据处理的动态自适应缓存策略研究 Study on Dynamic Adaptive Caching Strategy for Streaming Data Processing 计算机科学, 2020, 47(11): 122-127. https://doi.org/10.11896/jsjkx.190800093 |
| [7] | 胡海根, 孔祥勇, 周乾伟, 管秋, 陈胜勇. 基于深层卷积残差网络集成的黑色素瘤分类方法 Melanoma Classification Method by Integrating Deep Convolutional Residual Network 计算机科学, 2019, 46(5): 247-253. https://doi.org/10.11896/j.issn.1002-137X.2019.05.038 |
| [8] | 高山,刘炜,崔勇,张茜,王宗敏. 一种融合多种用户行为的协同过滤推荐算法 Collaborative Filtering Algorithm Integrating Multiple User Behaviors 计算机科学, 2016, 43(9): 227-231. https://doi.org/10.11896/j.issn.1002-137X.2016.09.045 |
| [9] | 李军伟,刘先省. 基于向量冲突表示方法的证据组合规则 Evidence Combination Rule Based on Vector Conflict Representation Method 计算机科学, 2016, 43(12): 58-62. https://doi.org/10.11896/j.issn.1002-137X.2016.12.010 |
| [10] | 李鹏飞,吴为民. 基于混合模型推荐算法的优化 Optimized Implementation of Hybrid Recommendation Algorithm 计算机科学, 2014, 41(2): 68-71. |
| [11] | 汤胤 彭宏 郑启伦. 一个基于范例推理的时序预测模型 计算机科学, 2004, 31(8): 103-106. |
|
||
首页