Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems
Abiri, Najmeh LU ; Linse, Björn LU ; Edén, Patrik LU and Ohlsson, Mattias LU
(2019)
In Neurocomputing
365.
p.137-146
- Abstract
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... (More)
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.
(Less)
- author
- Abiri, Najmeh
LU
; Linse, Björn
LU
; Edén, Patrik
LU
and Ohlsson, Mattias
LU
- organization
- publishing date
- 2019-11-06
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Autoencoder, Deep learning, Imputation, Missing data
- in
- Neurocomputing
- volume
- 365
- pages
- 10 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:85069939556
- ISSN
- 0925-2312
- DOI
- 10.1016/j.neucom.2019.07.065
- language
- English
- LU publication?
- yes
- id
- f10ae285-9eb9-41b5-a2d2-bca90974ddd8
- date added to LUP
- 2019-08-26 15:31:06
- date last changed
- 2024-04-02 15:54:49
@article{f10ae285-9eb9-41b5-a2d2-bca90974ddd8,
abstract = {{<p>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.</p>}},
author = {{Abiri, Najmeh and Linse, Björn and Edén, Patrik and Ohlsson, Mattias}},
issn = {{0925-2312}},
keywords = {{Autoencoder; Deep learning; Imputation; Missing data}},
language = {{eng}},
month = {{11}},
pages = {{137--146}},
publisher = {{Elsevier}},
series = {{Neurocomputing}},
title = {{Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems}},
url = {{http://dx.doi.org/10.1016/j.neucom.2019.07.065}},
doi = {{10.1016/j.neucom.2019.07.065}},
volume = {{365}},
year = {{2019}},
}