面向医疗缺失数据的选择性学习

韩宗博; 西雨晗; 盛培卓; 廖芸; 张长青

doi:10.12263/DZXB.20260303

您当前的位置：

首页 >

文章列表页 >

面向医疗缺失数据的选择性学习

学术论文 | 更新时间：2026-07-30

- 面向医疗缺失数据的选择性学习
- Selective Learning for Incomplete Medical Data
- 电子学报 2026年54卷第4期页码：1775-1788
- 作者机构：
  
  1.北京邮电大学信息与通信工程学院，北京 100876
  2.天津科技大学人工智能学院，天津 300457
  3.天津大学人工智能学院，天津 300354
- 作者简介：
  
  韩宗博男，1997年7月出生于山东省德州市。北京邮电大学信息与通信工程学院助理教授、硕士生导师。主要研究方向为多模态机器学习和智能医疗。中国电子学会会员编号：E190188742M。E-mail: zongbo@bupt.edu.cn
  西雨晗女，2005年9月出生于河南省三门峡市。现为天津科技大学人工智能学院本科生。主要研究方向为机器学习。E-mail: xiyuhan@mail.tust.edu.cn
  盛培卓女，2000年5月出生于天津市。天津大学人工智能学院硕士研究生。主要研究方向为机器学习与数据挖掘。E-mail: peizhuosheng@tju.edu.cn
  廖芸女，1991年5月出生于天津市。2024年博士毕业于天津大学，现任天津科技大学人工智能学院讲师。主要研究方向为在线学习、多模态医疗图像融合、大模型推理与自演化智能体。E-mail: yliao@tust.edu.cn
  张长青男，1982年8月出生于河南省安阳市。天津大学人工智能学院教授、博士生导师。主要研究方向为机器学习、计算机视觉、智能医疗。中国电子学会会员编号：E190188589M。E-mail: zhangchangqing@tju.edu.cn
- 基金信息：
  
  国家重点研发计划(2025YFF0515300);国家自然科学基金(624B2100)
- DOI：10.12263/DZXB.20260303
  中图分类号： TP181;
- 收稿：2026-03-27，
  
  录用：2026-04-08，
  
  网络首发：2026-05-20，
  
  纸质出版：2026-04-25
- 稿件说明：
移动端阅览
韩宗博, 西雨晗, 盛培卓, 等. 面向医疗缺失数据的选择性学习[J]. 电子学报, 2026, 54(04): 1775-1788.

HAN Zongbo, XI Yuhan, SHENG Peizhuo, et al. Selective Learning for Incomplete Medical Data[J]. Acta Electronica Sinica, 2026, 54(04): 1775-1788.
韩宗博, 西雨晗, 盛培卓, 等. 面向医疗缺失数据的选择性学习[J]. 电子学报, 2026, 54(04): 1775-1788. DOI：10.12263/DZXB.20260303

HAN Zongbo, XI Yuhan, SHENG Peizhuo, et al. Selective Learning for Incomplete Medical Data[J]. Acta Electronica Sinica, 2026, 54(04): 1775-1788. DOI：10.12263/DZXB.20260303

摘要

现实世界中的数据普遍存在缺失，此类数据缺失问题会显著劣化机器学习模型的性能表现。应对数据缺失的代表性方法为数据插补，即基于已观测到的数据对缺失数据进行估计，进而利用插补后的完整数据开展模型训练。然而，若插补过程中存在估计偏差，则可能引入额外噪声，往往会阻碍模型的学习过程。例如，低质量的插补往往带有估计偏差，这会影响数据的真实分布。模型若基于这些有偏样本建立映射关系，将难以捕捉数据的本质规律，进而导致泛化性能显著衰减。为此，本文提出了一种新的处理缺失数据的方法——面向缺失数据的选择性学习（Selective Learning for Incomplete Data，SLID），旨在有效利用缺失数据进行学习。SLID致力于构建一个能够动态感知数据质量的可靠学习框架。该方法在训练过程中引入动态评估机制，将被动接受插补数据转变为主动筛选：对于高置信度的插补样本，模型将其作为有效特征进行充分学习；而对于低置信度的样本，则引入正则化策略将其视为不可靠信息进行平滑处理。通过这种选择性学习机制，有效地规避了对有偏分布的过拟合，从而显著提升了泛化性能。基于多个数据集上的大量实验结果表明，与以往方法相比，SLID在准确性与可靠性方面均取得了显著提升。

Abstract

In real-world applications

data commonly suffer from missing values

which can substantially degrade the performance of machine learning models. A widely adopted approach to handling missing data is data imputation

where missing entries are estimated from observed data and models are subsequently trained on the completed dataset. However

imputation inevitably introduces estimation errors

and inaccurate imputations may inject additional noise that hinders effective learning. In particular

low-quality imputations often exhibit systematic bias

distorting the true underlying data distribution. When models are trained on such biased samples

they struggle to capture the intrinsic data-generating patterns

resulting in a pronounced decline in generalization performance. To address this issue

we propose a novel framework for learning with missing data

termed selective learning for incomplete data (SLID)

which aims to more effectively exploit incomplete data during model training. SLID is designed as a reliable learning paradigm that dynamically accounts for data quality. Specifically

it introduces a dynamic assessment mechanism that transforms the conventional passive reliance on imputed data into an active selection process. Imputed samples with high confidence are treated as reliable and are fully leveraged for learning

whereas low-confidence samples are regarded as unreliable and are accordingly smoothed through regularization strategies. Through this selective learning mechanism

retaining informative components while suppressing misleading ones

SLID effectively avoids overfitting to biased distributions and substantially enhances generalization performance. Extensive experimental results on multiple datasets demonstrate that

compared with existing approaches

SLID achieves significant improvements in both accuracy and reliability.

关键词

Keywords

references

Little R J A , Rubin D B . Statistical analysis with missing data [M ] . 3rd ed . Hoboken : John Wiley & Sons , 2019 . DOI: 10.1002/9781119482260 http://dx.doi.org/10.1002/9781119482260

Marlin B M , Kale D C , Khemani R G , et al . Unsupervised pattern discovery in electronic health care data using probabilistic clustering models [C ] // Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium . New York : ACM , 2012 : 389 - 398 . DOI: 10.1145/2110363.2110408 http://dx.doi.org/10.1145/2110363.2110408

韩悦 , 臧晓尧 , 李海 , 等 . 面向数据缺失场景的网络性能评估方法 [J ] . 通信学报 , 2025 , 46 ( 12 ): 199 - 212 . DOI: 10.11959/j.issn.1000-436x.2025202 http://dx.doi.org/10.11959/j.issn.1000-436x.2025202

Han Yue , Zang Xiaoyao , Li Hai , et al . Network performance evaluation method for scenarios with missing data [J ] . Journal on Communication , 2025 , 46 ( 12 ): 199 - 212 . (in Chinese) . DOI: 10.11959/j.issn.1000-436x.2025202 http://dx.doi.org/10.11959/j.issn.1000-436x.2025202

Schafer J L , Graham J W . Missing data: Our view of the state of the art [J ] . Psychological Methods , 2002 , 7 ( 2 ): 147 - 177 . DOI: 10.1037/1082-989x.7.2.147 http://dx.doi.org/10.1037/1082-989x.7.2.147

Schafer J L . Analysis of incomplete multivariate data [M ] . New York : CRC Press , 1997 . DOI: 10.1201/9781439821862 http://dx.doi.org/10.1201/9781439821862

Van Buuren S . Flexible imputation of missing data [M ] . 2nd ed . Boca Raton : CRC Press , 2018 . DOI: 10.1080/01621459.2019.1662249 http://dx.doi.org/10.1080/01621459.2019.1662249

Wabina R S , Looareesuwan P , Sonsilphong S , et al . Uncertainty-aware approach for multiple imputation using conventional and machine learning models: A real-world data study [J ] . Journal of Big Data , 2025 , 12 ( 1 ): 95 . DOI: 10.1186/s40537-025-01136-3 http://dx.doi.org/10.1186/s40537-025-01136-3

White I R , Royston P , Wood A M . Multiple imputation using chained equations: Issues and guidance for practice [J ] . Statistics in Medicine , 2011 , 30 ( 4 ): 377 - 399 . DOI: 10.1002/sim.4067 http://dx.doi.org/10.1002/sim.4067

Detrano R , Janosi A , Steinbrunn W , et al . International application of a new probability algorithm for the diagnosis of coronary artery disease [J ] . The American Journal of Cardiology , 1989 , 64 ( 5 ): 304 - 310 . DOI: 10.1016/0002-9149(89)90524-9 http://dx.doi.org/10.1016/0002-9149(89)90524-9

Troyanskaya O , Cantor M , Sherlock G , et al . Missing value estimation methods for DNA microarrays [J ] . Bioinformatics , 2001 , 17 ( 6 ): 520 - 525 . DOI: 10.1093/bioinformatics/17.6.520 http://dx.doi.org/10.1093/bioinformatics/17.6.520

Sarker I H . Machine learning: Algorithms, real-world applications and research directions [J ] . SN Computer Science , 2021 , 2 ( 3 ): 160 . DOI: 10.1007/s42979-021-00592-x http://dx.doi.org/10.1007/s42979-021-00592-x

Du Mengnan , Liu Ninghao , Yang Fan , et al . Learning credible deep neural networks with rationale regularization [C ] // Proceedings of the 2019 IEEE International Conference on Data Mining . Piscataway : IEEE , 2019 : 150 - 159 . DOI: 10.1109/ICDM.2019.00025 http://dx.doi.org/10.1109/ICDM.2019.00025

Müller R , Kornblith S , Hinton G E , et al . When does label smoothing help [C ] // Proceedings of the 33rd International Conference on Neural Information Processing Systems . Red Hook : Curran Associates , 2019 : 422 . DOI: 10.48550/arXiv.1906.02629 http://dx.doi.org/10.48550/arXiv.1906.02629

Pereyra G , Tucker G , Chorowski J , et al . Regularizing neural networks by penalizing confident output distributions [C ] // Proceedings of the 5th International Conference on Learning Representations . Toulon, France : OpenReview.net , 2017 . DOI: 10.48550/arXiv.1701.06548 http://dx.doi.org/10.48550/arXiv.1701.06548

Wang Dengbao , Feng Lei , Zhang Minling , et al . Rethinking calibration of deep neural networks: Do not be afraid of overconfidence [C ] // Proceedings of the 35th International Conference on Neural Information Processing Systems . Red Hook : Curran Associates , 2021 : 903 .

贾洁茹 , 张硕蕊 , 钱宇华 , 等 . 基于伪标签正则化损失的无监督行人重识别 [J ] . 电子学报 , 2024 , 52 ( 5 ): 1743 - 1758 .

Jia Jieru , Zhang Shuorui , Qian Yuhua , et al . Unsupervised person re-identification with pseudo label regularization loss [J ] . Acta Electronica Sinica , 2024 , 52 ( 5 ): 1743 - 1758 . (in Chinese)

Vapnik V N . Statistical learning theory [M ] . New York : Wiley , 1998 .

Bai Yichen , Han Zongbo , Cao Bing , et al . ID-like prompt learning for few-shot out-of-distribution detection [C ] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2024 : 17480 - 17489 . DOI: 10.1109/cvpr52733.2024.01655 http://dx.doi.org/10.1109/cvpr52733.2024.01655

Han Ruisong , Han Zongbo , Zhang Jiahao , et al . Retrieval-augmented prompt for OOD detection [PP/OL ] . V1. arXiv ( 2025-08-14 )[ 2026-03-20 ] . https://arxiv.org/abs/2508.10556 https://arxiv.org/abs/2508.10556 . DOI: 10.2139/ssrn.5430212 http://dx.doi.org/10.2139/ssrn.5430212

Zhang Qingyang , Wei Yake , Han Zongbo , et al . Multimodal fusion on low-quality data: A comprehensive survey [PP/OL ] . V3.arXiv ( 2024-04-27 )[ 2026-03-20 ] . https://arxiv.org/abs/2404.18947 https://arxiv.org/abs/2404.18947 . DOI: 10.1016/j.inffus.2026.104437 http://dx.doi.org/10.1016/j.inffus.2026.104437

贾熹滨 , 杨川旭 , 范超 , 等 . SM3RNet: 一种基于空间Mamba的模态缺失鲁棒病灶分割网络 [J ] . 电子学报 , 2025 , 53 ( 11 ): 3943 - 3955 .

Jia Xibin , Yang Chuanxu , Fan Chao , et al . SM3RNet: Spatial Mamba based missing modality robust lesion segmentation network [J ] . Acta Electronica Sinica , 2025 , 53 ( 11 ): 3943 - 3955 . (in Chinese)

De Leeuw E D . Reducing missing data in surveys: An overview of methods [J ] . Quality and Quantity , 2001 , 35 ( 2 ): 147 - 160 . DOI: 10.1023/a:1010395805406 http://dx.doi.org/10.1023/a:1010395805406

Glance L G , Osler T M , Mukamel D B , et al . Impact of statistical approaches for handling missing data on trauma center quality [J ] . Annals of Surgery , 2009 , 249 ( 1 ): 143 - 148 . DOI: 10.1097/sla.0b013e31818e544b http://dx.doi.org/10.1097/sla.0b013e31818e544b

Allison P D . Missing data [M ] // The SAGE handbook of quantitative methods in psychology . London : SAGE Publications , 2009 : 72 - 89 . DOI: 10.4135/9780857020994.n4 http://dx.doi.org/10.4135/9780857020994.n4

Young W , Weckman G , Holland W . A survey of methodologies for the treatment of missing values within datasets: Limitations and benefits [J ] . Theoretical Issues in Ergonomics Science , 2011 , 12 ( 1 ): 15 - 43 . DOI: 10.1080/14639220903470205 http://dx.doi.org/10.1080/14639220903470205

Bertsimas D , Pawlowski C , Zhuo Y D . From predictive methods to missing data imputation: An optimization approach [J ] . Journal of Machine Learning Research , 2017 , 18 ( 1 ): 7133 - 7171 .

Graham J W . Missing data analysis: Making it work in the real world [J ] . Annual Review of Psychology , 2009 , 60 : 549 - 576 . DOI: 10.1146/annurev.psych.58.110405.085530 http://dx.doi.org/10.1146/annurev.psych.58.110405.085530

Pigott T D . A review of methods for missing data [J ] . Educational Research and Evaluation , 2001 , 7 ( 4 ): 353 - 383 . DOI: 10.1076/edre.7.4.353.8937 http://dx.doi.org/10.1076/edre.7.4.353.8937

Acuña E , Rodriguez C . The treatment of missing values and its effect on classifier accuracy [M ] // Classification, clustering, and data mining applications . Berlin : Springer , 2004 : 639 - 647 . DOI: 10.1007/978-3-642-17103-1_60 http://dx.doi.org/10.1007/978-3-642-17103-1_60

Cheng Lyu , Antoniou C . A diffusion-based expectation-maximization framework for probabilistic traffic data imputation [J ] . IEEE Internet of Things Journal , 2025 , 12 ( 20 ): 43227 - 43240 . DOI: 10.1109/jiot.2025.3594996 http://dx.doi.org/10.1109/jiot.2025.3594996

梅雅欣 , 秦慧玲 , 梁玉珠 , 等 . 基于大语言模型的时空数据零样本插补 [J ] . 电子学报 , 2025 , 53 ( 9 ): 3047 - 3059 .

Mei Yaxin , Qin Huiling , Liang Yuzhu , et al . LLM-based zero-shot imputation of spatiotemporal data [J ] . Acta Electronica Sinica , 2025 , 53 ( 9 ): 3047 - 3059 . (in Chinese)

Hastie T , Tibshirani R , Friedman J H . The elements of statistical learning: Data mining, inference, and prediction [M ] . 2nd ed . New York : Springer , 2009 .

Moradi R , Berangi R , Minaei B . A survey of regularization strategies for deep models [J ] . Artificial Intelligence Review , 2020 , 53 ( 6 ): 3947 - 3986 . DOI: 10.1007/s10462-019-09784-7 http://dx.doi.org/10.1007/s10462-019-09784-7

Han Zongbo , Zhang Changqing , Fu Huazhu , et al . Trusted multi-view classification with dynamic evidential fusion [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023 , 45 ( 2 ): 2551 - 2566 . DOI: 10.1109/tpami.2022.3171983 http://dx.doi.org/10.1109/tpami.2022.3171983

Zhang Chiyuan , Bengio S , Hardt M , et al . Understanding deep learning (still) requires rethinking generalization [J ] . Communications of the ACM , 2021 , 64 ( 3 ): 107 - 115 . DOI: 10.1145/3446776 http://dx.doi.org/10.1145/3446776

Hernández-García A , König P . Data augmentation instead of explicit regularization [PP/OL ] . V5. arXiv ( 2018-06-11 )[ 2026-03-20 ] . https://arxiv.org/abs/1806.03852v5 https://arxiv.org/abs/1806.03852v5 .

Zheng Qinghe , Yang Mingqiang , Yang Jiajie , et al . Improvement of generalization ability of deep CNN via implicit regularization in two-stage training process [J ] . IEEE Access , 2018 , 6 : 15844 - 15869 . DOI: 10.1109/access.2018.2810849 http://dx.doi.org/10.1109/access.2018.2810849

Tian Yingjie , Zhang Yuqi . A comprehensive survey on regularization strategies in machine learning [J ] . Information Fusion , 2022 , 80 : 146 - 166 . DOI: 10.1016/j.inffus.2021.11.005 http://dx.doi.org/10.1016/j.inffus.2021.11.005

Lin T Y , Goyal P , Girshick R , et al . Focal loss for dense object detection [C ] // Proceedings of the IEEE International Conference on Computer Vision . Piscataway : IEEE , 2017 : 2999 - 3007 . DOI: 10.1109/iccv.2017.324 http://dx.doi.org/10.1109/iccv.2017.324

Tao Linwei , Dong Mingjing , Xu Chang . Dual focal loss for calibration [C ] // Proceedings of the 40th International Conference on Machine Learning . Honolulu : PMLR , 2023 : 33833 - 33849 .

Yu Xiaotong , Sun Shiding , Tian Yingjie . Sample selection for noisy partial label learning with interactive contrastive learning [J ] . Pattern Recognition , 2025 , 166 : 111681 . DOI: 10.1016/j.patcog.2025.111681 http://dx.doi.org/10.1016/j.patcog.2025.111681

贺前华 , 陈永强 , 郑若伟 , 等 . 基于样本类不确定性抽样的端到端语音关键词检测训练方法 [J ] . 电子学报 , 2024 , 52 ( 10 ): 3482 - 3492 . DOI: 10.12263/DZXB.20240048 http://dx.doi.org/10.12263/DZXB.20240048

He Qianhua , Chen Yongqiang , Zheng Ruowei , et al . End-to-end speech keyword spotting training method based on sample’s class uncertainty [J ] . Acta Electronica Sinica , 2024 , 52 ( 10 ): 3482 - 3492 . (in Chinese) . DOI: 10.12263/DZXB.20240048 http://dx.doi.org/10.12263/DZXB.20240048

王海荣 , 王彤 , 徐玺 , 等 . CLGLF: 置信学习引导标签融合的多模态命名实体识别方法 [J ] . 电子学报 , 2024 , 52 ( 7 ): 2429 - 2437 . DOI: 10.12263/DZXB.20231160 http://dx.doi.org/10.12263/DZXB.20231160

Wang Hairong , Wang Tong , Xu Xi , et al . CLGLF: Confidence learning guided label fusion for multimodal named entity recognition method [J ] . Acta Electronica Sinica , 2024 , 52 ( 7 ): 2429 - 2437 . (in Chinese) . DOI: 10.12263/DZXB.20231160 http://dx.doi.org/10.12263/DZXB.20231160

Han Zongbo , Yang Yifeng , Zhang Changqing , et al . Selective learning: Towards robust calibration with dynamic regularization [PP/OL ] . V2. arXiv ( 2024-02-13 )[ 2026-03-20 ] . https://arxiv.org/abs/2402.08384 https://arxiv.org/abs/2402.08384 .

Breiman L . Random forests [J ] . Machine Learning , 2001 , 45 ( 1 ): 5 - 32 . DOI: 10.1023/a:1010933404324 http://dx.doi.org/10.1023/a:1010933404324

Goodman N R . Statistical analysis based on a certain multivariate complex Gaussian distribution (an introduction) [J ] . The Annals of Mathematical Statistics , 1963 , 34 ( 1 ): 152 - 177 . DOI: 10.1214/aoms/1177704250 http://dx.doi.org/10.1214/aoms/1177704250

Bloch D A , Watson G S . A Bayesian study of the multinomial distribution [J ] . The Annals of Mathematical Statistics , 1967 , 38 ( 5 ): 1423 - 1435 . DOI: 10.1214/aoms/1177698697 http://dx.doi.org/10.1214/aoms/1177698697

Huber P J . Robust estimation of a location parameter [M ] // Breakthroughs in statistics: Methodology and distribution . New York : Springer , 1992 : 492 - 518 . DOI: 10.1007/978-1-4612-4380-9_35 http://dx.doi.org/10.1007/978-1-4612-4380-9_35

Shannon C E . A mathematical theory of communication [J ] . The Bell System Technical Journal , 1948 , 27 ( 3 ): 379 - 423 . DOI: 10.1002/j.1538-7305.1948.tb01338.x http://dx.doi.org/10.1002/j.1538-7305.1948.tb01338.x

Lecun Y , Bengio Y , Hinton G . Deep learning [J ] . Nature , 2015 , 521 ( 7553 ): 436 - 444 . DOI: 10.1038/nature14539 http://dx.doi.org/10.1038/nature14539

Kullback S , Leibler R A . On information and sufficiency [J ] . The Annals of Mathematical Statistics , 1951 , 22 ( 1 ): 79 - 86 . DOI: 10.1214/aoms/1177729694 http://dx.doi.org/10.1214/aoms/1177729694

Patrício M , Pereira J , Crisóstomo J , et al . Using Resistin, glucose, age and BMI to predict the presence of breast cancer [J ] . BMC Cancer , 2018 , 18 ( 1 ): 29 . DOI: 10.1186/s12885-017-3877-1 http://dx.doi.org/10.1186/s12885-017-3877-1

Ma Yuzhe , Zhu Xiaojin , Hsu J . Data poisoning against differentially-private learners: Attacks and defenses [C ] // Proceedings of the 28th International Joint Conference on Artificial Intelligence . Macao, China : IJCAI , 2019 : 4732 - 4738 . DOI: 10.24963/ijcai.2019/657 http://dx.doi.org/10.24963/ijcai.2019/657

Uddin M N , Halder R K . An ensemble method based multilayer dynamic system to predict cardiovascular disease using machine learning approach [J ] . Informatics in Medicine Unlocked , 2021 , 24 : 100584 . DOI: 10.1016/j.imu.2021.100584 http://dx.doi.org/10.1016/j.imu.2021.100584

Neyman J , Pearson E S . On the problem of the most efficient tests of statistical hypotheses [J ] . Philosophical Transactions of the Royal Society of London Series A, Containing Papers of a Mathematical or Physical Character , 1933 , 231 ( 694/695/696/697/698/699/700/701/702/703/704/705/706 ): 289 - 337 . DOI: 10.1098/rsta.1933.0009 http://dx.doi.org/10.1098/rsta.1933.0009

Hanley J A , McNeil B J . The meaning and use of the area under a receiver operating characteristic (ROC) curve [J ] . Radiology , 1982 , 143 ( 1 ): 29 - 36 . DOI: 10.1148/radiology.143.1.7063747 http://dx.doi.org/10.1148/radiology.143.1.7063747

Youden W J . Index for rating diagnostic tests [J ] . Cancer , 1950 , 3 ( 1 ): 32 - 35 . DOI: 10.1002/1097-0142(1950)3:1<32::aid-cncr2820030106>3.0.co;2-3 http://dx.doi.org/10.1002/1097-0142(1950)3:1<32::aid-cncr2820030106>3.0.co;2-3

Lewis D D . A sequential algorithm for training text classifiers: Corrigendum and additional data [J ] . ACM SIGIR Forum , 1995 , 29 ( 2 ): 13 - 19 . DOI: 10.1145/219587.219592 http://dx.doi.org/10.1145/219587.219592

Guo Chuan , Pleiss G , Sun Yu , et al . On calibration of modern neural networks [C ] // Proceedings of the 34th International Conference on Machine Learning . Sydney : PMLR , 2017 : 1321 - 1330 . DOI: 10.48550/arXiv.1706.04599 http://dx.doi.org/10.48550/arXiv.1706.04599

Brier G W . Verification of forecasts expressed in terms of probability [J ] . Monthly Weather Review , 1950 , 78 ( 1 ): 1 - 3 . DOI: 10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2 http://dx.doi.org/10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2

Van Buuren S , Groothuis-Oudshoorn K . mice: Multivariate imputation by chained equations in R [J ] . Journal of Statistical Software , 2011 , 45 ( 3 ): 1 - 67 . DOI: 10.18637/jss.v045.i03 http://dx.doi.org/10.18637/jss.v045.i03

Yoon J , Jordon J , van der Schaar M . GAIN: Missing data imputation using generative adversarial nets [C ] // Proceedings of the 35th International Conference on Machine Learning . Stockholm, Sweden : PMLR , 2018 : 5675 - 5684 . DOI: 10.48550/arXiv.1806.02920 http://dx.doi.org/10.48550/arXiv.1806.02920

Mattei P A , Frellsen J . MIWAE: Deep generative modelling and imputation of incomplete data sets [C ] // Proceedings of the 36th International Conference on Machine Learning . Long Beach : PMLR , 2019 : 4413 - 4423 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

用于亚纳秒级瞬态脉冲测量的宽频电流探头的设计和校准方法

基于多智能体强化学习的源荷分离方法

射频功率放大器行为模型基函数自适应生成技术

PCDA-MPAT：物理约束数据增强与多径Transformer的无源物联网室内定位方法

基于动态图神经网络与对比学习的单通道同频盲信号分离方法