TY - JOUR
T1 - Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems
AU - Abiri, Najmeh
AU - Linse, Björn
AU - Edén, Patrik
AU - Ohlsson, Mattias
PY - 2019/11/6
Y1 - 2019/11/6
N2 - Dealing with missing data in data analysis is inevitable. Although powerful imputation methods that address this problem exist, there is still much room for improvement. In this study, we examined single imputation based on deep autoencoders, motivated by the apparent success of deep learning to efficiently extract useful dataset features. We have developed a consistent framework for both training and imputation. Moreover, we benchmarked the results against state-of-the-art imputation methods on different data sizes and characteristics. The work was not limited to the one-type variable dataset; we also imputed missing data with multi-type variables, e.g., a combination of binary, categorical, and continuous attributes. To evaluate the imputation methods, we randomly corrupted the complete data, with varying degrees of corruption, and then compared the imputed and original values. In all experiments, the developed autoencoder obtained the smallest error for all ranges of initial data corruption.
AB - Dealing with missing data in data analysis is inevitable. Although powerful imputation methods that address this problem exist, there is still much room for improvement. In this study, we examined single imputation based on deep autoencoders, motivated by the apparent success of deep learning to efficiently extract useful dataset features. We have developed a consistent framework for both training and imputation. Moreover, we benchmarked the results against state-of-the-art imputation methods on different data sizes and characteristics. The work was not limited to the one-type variable dataset; we also imputed missing data with multi-type variables, e.g., a combination of binary, categorical, and continuous attributes. To evaluate the imputation methods, we randomly corrupted the complete data, with varying degrees of corruption, and then compared the imputed and original values. In all experiments, the developed autoencoder obtained the smallest error for all ranges of initial data corruption.
KW - Autoencoder
KW - Deep learning
KW - Imputation
KW - Missing data
UR - http://www.scopus.com/inward/record.url?scp=85069939556&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2019.07.065
DO - 10.1016/j.neucom.2019.07.065
M3 - Article
AN - SCOPUS:85069939556
SN - 0925-2312
VL - 365
SP - 137
EP - 146
JO - Neurocomputing
JF - Neurocomputing
ER -