DEGAIN: Generative-Adversarial-Network-Based Missing Data Imputation
Insights and analysis are only as good as the available data. Data cleaning is one of the most important steps to create quality data decision making. Machine learning (ML) helps deal with data quickly, and to create error-free or limited-error datasets. One of the quality standards for cleaning the...
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MDPI AG
2022-12-01
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Online Access: | https://www.mdpi.com/2078-2489/13/12/575 |
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author | Reza Shahbazian Irina Trubitsyna |
author_facet | Reza Shahbazian Irina Trubitsyna |
author_sort | Reza Shahbazian |
collection | DOAJ |
description | Insights and analysis are only as good as the available data. Data cleaning is one of the most important steps to create quality data decision making. Machine learning (ML) helps deal with data quickly, and to create error-free or limited-error datasets. One of the quality standards for cleaning the data includes handling the missing data, also known as data imputation. This research focuses on the use of machine learning methods to deal with missing data. In particular, we propose a generative adversarial network (GAN) based model called DEGAIN to estimate the missing values in the dataset. We evaluate the performance of the presented method and compare the results with some of the existing methods on publicly available Letter Recognition and SPAM datasets. The Letter dataset consists of 20,000 samples and 16 input features and the SPAM dataset consists of 4601 samples and 57 input features. The results show that the proposed DEGAIN outperforms the existing ones in terms of root mean square error and Frechet inception distance metrics. |
first_indexed | 2024-03-09T16:17:24Z |
format | Article |
id | doaj.art-0a2e2a726765454380effc1b9ada05e7 |
institution | Directory Open Access Journal |
issn | 2078-2489 |
language | English |
last_indexed | 2024-03-09T16:17:24Z |
publishDate | 2022-12-01 |
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record_format | Article |
series | Information |
spelling | doaj.art-0a2e2a726765454380effc1b9ada05e72023-11-24T15:37:22ZengMDPI AGInformation2078-24892022-12-01131257510.3390/info13120575DEGAIN: Generative-Adversarial-Network-Based Missing Data ImputationReza Shahbazian0Irina Trubitsyna1Department of Informatics, Modeling, Electronics and System Engineering, University of Calabria, 87036 Rende, ItalyDepartment of Informatics, Modeling, Electronics and System Engineering, University of Calabria, 87036 Rende, ItalyInsights and analysis are only as good as the available data. Data cleaning is one of the most important steps to create quality data decision making. Machine learning (ML) helps deal with data quickly, and to create error-free or limited-error datasets. One of the quality standards for cleaning the data includes handling the missing data, also known as data imputation. This research focuses on the use of machine learning methods to deal with missing data. In particular, we propose a generative adversarial network (GAN) based model called DEGAIN to estimate the missing values in the dataset. We evaluate the performance of the presented method and compare the results with some of the existing methods on publicly available Letter Recognition and SPAM datasets. The Letter dataset consists of 20,000 samples and 16 input features and the SPAM dataset consists of 4601 samples and 57 input features. The results show that the proposed DEGAIN outperforms the existing ones in terms of root mean square error and Frechet inception distance metrics.https://www.mdpi.com/2078-2489/13/12/575machine learningdata cleaningmissing datadata imputationgenerative networks |
spellingShingle | Reza Shahbazian Irina Trubitsyna DEGAIN: Generative-Adversarial-Network-Based Missing Data Imputation Information machine learning data cleaning missing data data imputation generative networks |
title | DEGAIN: Generative-Adversarial-Network-Based Missing Data Imputation |
title_full | DEGAIN: Generative-Adversarial-Network-Based Missing Data Imputation |
title_fullStr | DEGAIN: Generative-Adversarial-Network-Based Missing Data Imputation |
title_full_unstemmed | DEGAIN: Generative-Adversarial-Network-Based Missing Data Imputation |
title_short | DEGAIN: Generative-Adversarial-Network-Based Missing Data Imputation |
title_sort | degain generative adversarial network based missing data imputation |
topic | machine learning data cleaning missing data data imputation generative networks |
url | https://www.mdpi.com/2078-2489/13/12/575 |
work_keys_str_mv | AT rezashahbazian degaingenerativeadversarialnetworkbasedmissingdataimputation AT irinatrubitsyna degaingenerativeadversarialnetworkbasedmissingdataimputation |