A Comparison of Machine Learning Methods for Data Imputation-Reference-Cited by-同舟云学术

A Comparison of Machine Learning Methods for Data Imputation

Published:2020-09 Issue: Volume: Page:
ISSN:
Container-title:11th Hellenic Conference on Artificial Intelligence
language:
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Author:

Platias Christos¹,Petasis Georgios¹

Affiliation:

1. National Center for Scientific Research 'Demokritos', Greece

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3411408.3411465

Reference42 articles.

1. M. Albinsson and E. Gillsbro. 2017. Imputation Methods in Dialysis Data. Lund University. M. Albinsson and E. Gillsbro. 2017. Imputation Methods in Dialysis Data. Lund University.

2. Gustavo Batista and Maria Carolina Monard. 2003. A Study of K-Nearest Neighbour as an Imputation Method. In In HIS. Gustavo Batista and Maria Carolina Monard. 2003. A Study of K-Nearest Neighbour as an Imputation Method. In In HIS.

3. Stef van Buuren. 2012. Flexible Imputation of Missing Data. Chapman and Hall/CRC. Stef van Buuren. 2012. Flexible Imputation of Missing Data. Chapman and Hall/CRC.

4. Wei Cao Dong Wang Jian Li Hao Zhou Lei Li and Yitan Li. 2018. BRITS: Bidirectional Recurrent Imputation for Time Series. In Advances in Neural Information Processing Systems 31 S. Bengio H. Wallach H. Larochelle K. Grauman N. Cesa-Bianchi and R. Garnett (Eds.). Curran Associates Inc. 6775–6785. Wei Cao Dong Wang Jian Li Hao Zhou Lei Li and Yitan Li. 2018. BRITS: Bidirectional Recurrent Imputation for Time Series. In Advances in Neural Information Processing Systems 31 S. Bengio H. Wallach H. Larochelle K. Grauman N. Cesa-Bianchi and R. Garnett (Eds.). Curran Associates Inc. 6775–6785.

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