Imputations of missing values using a tracking-removed autoencoder trained with incomplete data
Authors:
- Xiaochen LaiXLXiaochen Lai
- Xia WuXWXia Wu
- Liyong ZhangLZLiyong Zhang
- Wei LuWLWei Lu
- Chongquan ZhongCZChongquan Zhong
- 1 November 2019
- journal article
- research article
- Published by Elsevier in Neurocomputing
- Vol. 366, 54-65
Abstract
No abstract availableKeywords
Funding Information
- National Key R&D (2018YFB1700200)
This publication has 34 references indexed in Scilit:
- LSTM-based traffic flow prediction with missing dataNeurocomputing, 2018
- Recurrent Neural Networks for Multivariate Time Series with Missing ValuesScientific Reports, 2018
- Clinical information extraction applications: A literature reviewJournal of Biomedical Informatics, 2018
- Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of ThingsIEEE Access, 2017
- A Comparison of Multiple-Imputation Methods for Handling Missing Data in Repeated Measurements Observational StudiesJournal of the Royal Statistical Society Series A: Statistics in Society, 2015
- A Review of Hot Deck Imputation for Survey Non‐responseInternational Statistical Review, 2010
- Pattern classification with missing data: a reviewNeural Computing and Applications, 2009
- Impact of imputation of missing values on classification error for discrete dataPattern Recognition, 2008
- Review: A gentle introduction to imputation of missing valuesJournal of Clinical Epidemiology, 2006
- A review of methods for spike sorting: the detection and classification of neural action potentialsNetwork: Computation in Neural Systems, 1998