A Novel Nonparametric Multiple Imputation Algorithm for Estimating Missing Data

The treatment of incomplete data is an important step in pre-processing data prior to later analysis. We propose a novel non-parametric multiple imputation algorithm for estimating missing value. The proposed algorithm is based on Generalized Regression Neural Networks. We compare the proposed algorithm against existing algorithms on forty-five real and synthetic datasets. The effectiveness of imputation algorithms is evaluated in classification problems. The performance of proposed algorithm appears to be superior to that of other algorithms.