Entropy-Based Dynamic Imputation

Context

Entropy-Based Dynamic Imputation is based on Dynamic Imputation. Whereas the standard, non-entropy-based approaches randomly choose the sequence in which missing values are imputed within a row of data, the entropy-based methods select the order based on the conditional uncertainty associated with the unobserved variable.

More specifically, missing values are imputed first for those variables that meet the following conditions:

• Variables that have a fully-observed/imputed Markov Blanket.
• Variables that have the lowest conditional Entropy, given the observations and imputed values.

The advantages of the entropy-based methods are:

• The speed improvement over their corresponding standard methods.
• Their improved ability to handle datasets with large proportions of missing values.

Usage

Select Menus > Learning > Missing Values Processing > Entropy-Based Dynamic Imputation.