A random forest dynamic threshold imputation method for handling missing data in cognitive diagnosis assessments.

ABSTRACT: The handling of missing data in cognitive diagnostic assessment is an important issue. The Random Forest Threshold Imputation (RFTI) method proposed by You et al. in 2023 is specifically designed for cognitive diagnostic models (CDMs) and built on the random forest imputation. However, in RFTI, the threshold for determining imputed values to be 0 is fixed at 0.5, which may result in uncertainty in this imputation. To address this issue, we proposed an improved method, Random Forest Dynamic Threshold Imputation (RFDTI), which possess two dynamic thresholds for dichotomous imputed values. A simulation study showed that the classification of attribute profiles when using RFDTI to impute missing data was always better than the four commonly used traditional methods (i.e., person mean imputatio

SUBMITTER: You X 

PROVIDER: S-EPMC12362312 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature