A Two-Stage Missing Value Imputation Method Based on Autoencoder Neural Network

Abstract

Due to the ubiquitous presence of missing values in real-world datasets, an imputation algorithm can recover the missing values and provide users with a complete dataset that utilizes all the available observed information. However, most of the imputation methods still have several limitations, including cannot restore the original distribution, handling various data missing patterns, and high missing rate dataset. In this poster, a novel neural network-based two-stage missing value imputation (abbreviated as TS-MVI) method is proposed to fill an incomplete condition attribute with the optimized attribute values for the supervised learning task. By initializing the missing values with random numbers, the imputation values are iteratively adjusted based on the new updating rule by minimizing both the autoencoder-oriented objective function and neural network-based classification error. The persuasive experiments show that TS-MVl method significantly outperforms current state-of-the-art imputation methods and thus demonstrate TS-MVI is a viable approach to deal with the missing value imputation problem.