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The missing values amounted to 3.3%%.
The missing values have been multiply imputed using relevant econometric techniques.
Therefore, it is important to reduce the missing values caused by software bugs or calculation timeouts.
After deleting redundant features, we replace the missing values or wrong ones using the KNN method.
For samples with missing target labels, these scores can be used to replace the missing values.
Unlike Refs. [7, 23] did not ignore the missing values in the learning process.
Multiple imputations are repeated random draws from the predictive distribution of the missing values.
Thus, the decision was made to use raw data and not to impute the missing values.
MI is a missing data technique that imputes plausible values for the missing values.
One generates m datasets with plausible values imputed for the missing values.
For each abstracted variable with missing values, we first assessed the prevalence of the missing values.
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