J
Jastin Pompeu Soares
Researcher at University of Coimbra
Publications - 6
Citations - 345
Jastin Pompeu Soares is an academic researcher from University of Coimbra. The author has contributed to research in topics: Imputation (statistics) & Missing data. The author has an hindex of 4, co-authored 6 publications receiving 166 citations.
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Journal ArticleDOI
Cross-Validation for Imbalanced Datasets: Avoiding Overoptimistic and Overfitting Approaches [Research Frontier]
Miriam Seoane Santos,Jastin Pompeu Soares,Pedro Henrigues Abreu,Helder Araujo,João A. C. Santos +4 more
TL;DR: Although cross-validation is a standard procedure for performance evaluation, its joint application with oversampling remains an open question for researchers farther from the imbalanced data topic.
Journal ArticleDOI
Generating Synthetic Missing Data: A Review by Missing Mechanism
Miriam Seoane Santos,Ricardo Cardoso Pereira,Adriana Fonseca Costa,Jastin Pompeu Soares,João A. M. Santos,Pedro Henriques Abreu +5 more
TL;DR: The analysis revealed that creating missing at random and missing not at random scenarios in datasets comprising qualitative features is the most challenging issue in the related work and, therefore, should be the focus of future work in the field.
Book ChapterDOI
Missing Data Imputation via Denoising Autoencoders: The Untold Story
TL;DR: A comparison study between state-of-the-art imputation techniques and a Stacked Denoising Autoencoders approach showed that Support Vector Machines imputation ensures the best classification performance while Multiple Imputation by Chained Equations performs better in terms of imputation quality.
Book ChapterDOI
Influence of Data Distribution in Missing Data Imputation
Miriam Seoane Santos,Jastin Pompeu Soares,Pedro Henriques Abreu,Helder Araujo,João A. M. Santos +4 more
TL;DR: There is a relationship between features’ distribution and algorithms’ performance, although some factors must be taken into account, such as the number of features per distribution and the missing rate at state.
Book ChapterDOI
Exploring the Effects of Data Distribution in Missing Data Imputation
Jastin Pompeu Soares,Miriam Seoane Santos,Pedro Henriques Abreu,Helder Araujo,João A. M. Santos +4 more
TL;DR: There is a relationship between features’ distribution and algorithms’ performance, and that their performance seems to be affected by the combination of missing rate and scenario at state and also other less obvious factors such as sample size, goodness-of-fit of features and the ratio between the number of Features and the different distributions comprised in the dataset.