RT journal article
T1 A Perspective on the Missing at Random Problem: Synthetic Generation and Benchmark Analysis
A1 Cabrera Sánchez, Juan Francisco
A1 Cardoso Pereira, Ricardo
A1 Henriques Abreu, Pedro
A1 Silva Ramírez, Esther Lydia
A2 Ingeniería Informática
K1 Data preprocessing
K1 missing data
K1 missing at random
K1 synthetic missingness
K1 deep learning
K1 machine learning
K1 artificial intelligence
AB Progressively more advanced and complex models are proposed to address problems related to computer vision, forecasting, Internet of Things, Big Data and so on. However, these disciplines require preprocessing steps to obtain meaningful results. One of the most common problems addressed in this stage is the presence of missing values. Understanding the reason why missingness occurs helps to selectdata imputation methods that are more adequate to complete these missing values. Missing at Random synthetic generation presents challenges such as achieving extreme missingness rates and preserving the consistency of the mechanism. To address these shortcomings, three new methods that generate synthetic missingness under the Missing at Random mechanism are proposed in this work and compared to a baseline model. This comparison considers a benchmark covering 33 data sets and five missingness rates (10%, 20%, 40%, 60%, 80%). Seven data imputation methods are compared to evaluate the proposals, ranging from traditional methods to deep learning methods. The results demonstrate that the proposals are aligned with the baseline method in terms of the performance and ranking of data imputation methods.  Thus, three new feasible and consistent alternatives for synthetic missingness generation under Missing at random are presented.
PB IEEE
SN 2169-3536
YR 2024
FD 2024-11-12
LK http://hdl.handle.net/10498/34220
UL http://hdl.handle.net/10498/34220
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 11-nov-2026