Nested Random Forest: A Personalized Imputation Method for Missing Data-Reference-Cited by-同舟云学术

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Nested Random Forest: A Personalized Imputation Method for Missing Data

Published:2022-08-24 Issue: Volume: Page:
ISSN:
Container-title:2022 8th International Conference on Big Data and Information Analytics (BigDIA)
language:
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Author:

Wang Ke¹,Luo Menghua²,Deng Mingsen¹,Chen Haiwen³

Affiliation:

1. Information College Guizhou University of Finance and Economics,Guiyang,China

2. Big Data Application and Economics Colllege Guizhou University of Finance and Economics,Guiyang,China

3. College of Systems Engineering National University of Defense Technology,Changsha,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9873850/9873995/09874127.pdf?arnumber=9874127

Reference18 articles.

1. Missing data was handled inconsistently in UK prediction models: a review of method used

2. Application of imputation methods for missing values of PM10 and O3 data: Interpolation, moving average and K-nearest neighbor methods

3. Addressing Missing Data in a Healthcare Dataset Using an Improved kNN Algorithm

4. Imputation of missing precipitation data using KNN, SOM, RF, and FNN

5. GAIN: Missing data imputation using generative adversarial nets;yoon;Proceedings of the 35th International Conference on Machine Learning ser Proceedings of Machine Learning Research,2018

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