Missing Value Imputation Method by Using Bayesian Network with Weighted Learning

2012 ◽  
Vol 132 (2) ◽  
pp. 299-305
Author(s):  
Yoshihiro Miyakoshi ◽  
Shohei Kato
Keyword(s):  
Bayesian Network ◽  
Imputation Method ◽  
Missing Value ◽  
Missing Value Imputation

scholarly journals A data-driven missing value imputation approach for longitudinal datasets

2021 ◽  
Author(s):  
Caio Ribeiro ◽  
Alex A. Freitas
Keyword(s):  
Missing Data ◽  
Longitudinal Data ◽  
Missing Values ◽  
Error Rates ◽  
Imputation Method ◽  
Data Driven ◽  
Missing Value ◽  
Missing Value Imputation ◽  
Human Ageing ◽  
Imputation Approach

AbstractLongitudinal datasets of human ageing studies usually have a high volume of missing data, and one way to handle missing values in a dataset is to replace them with estimations. However, there are many methods to estimate missing values, and no single method is the best for all datasets. In this article, we propose a data-driven missing value imputation approach that performs a feature-wise selection of the best imputation method, using known information in the dataset to rank the five methods we selected, based on their estimation error rates. We evaluated the proposed approach in two sets of experiments: a classifier-independent scenario, where we compared the applicabilities and error rates of each imputation method; and a classifier-dependent scenario, where we compared the predictive accuracy of Random Forest classifiers generated with datasets prepared using each imputation method and a baseline approach of doing no imputation (letting the classification algorithm handle the missing values internally). Based on our results from both sets of experiments, we concluded that the proposed data-driven missing value imputation approach generally resulted in models with more accurate estimations for missing data and better performing classifiers, in longitudinal datasets of human ageing. We also observed that imputation methods devised specifically for longitudinal data had very accurate estimations. This reinforces the idea that using the temporal information intrinsic to longitudinal data is a worthwhile endeavour for machine learning applications, and that can be achieved through the proposed data-driven approach.

Effective Bayesian-network-based missing value imputation enhanced by crowdsourcing

2020 ◽  
Vol 190 ◽  
pp. 105199 ◽  
Author(s):  
Chen Ye ◽  
Hongzhi Wang ◽  
Wenbo Lu ◽  
Jianzhong Li
Keyword(s):  
Bayesian Network ◽  
Missing Value ◽  
Missing Value Imputation

BiLSTM-A: A missing value imputation method for PM2.5 prediction

2020 ◽  
Author(s):  
Nan Jiang ◽  
Yanan Li ◽  
Hua Zuo ◽  
Hui Zheng ◽  
Qinghe Zheng
Keyword(s):  
Imputation Method ◽  
Missing Value ◽  
Missing Value Imputation
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