This paper focuses on the use of machine learning methods to deal with missing data imputation. In particular, we describe a generative adversarial network (GAN) based model called DEGAIN to estimate the missing values in the dataset. We present the performance of the presented method comparing the results with some of the existing methods on the publicly available Letter Recognition and SPAM datasets. The Letter dataset consists of 20000 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.
DEGAIN as tool for Missing Data Imputation
Shahbazian R.;Trubitsyna I.
2023-01-01
Abstract
This paper focuses on the use of machine learning methods to deal with missing data imputation. In particular, we describe a generative adversarial network (GAN) based model called DEGAIN to estimate the missing values in the dataset. We present the performance of the presented method comparing the results with some of the existing methods on the publicly available Letter Recognition and SPAM datasets. The Letter dataset consists of 20000 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
