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Publicações

Publicações por Pedro Henriques Abreu

2018

Analysing the Footprint of Classifiers in Overlapped and Imbalanced Contexts

Autores
Mercier, M; Santos, MS; Abreu, PH; Soares, C; Soares, JP; Santos, J;

Publicação
Advances in Intelligent Data Analysis XVII - 17th International Symposium, IDA 2018, 's-Hertogenbosch, The Netherlands, October 24-26, 2018, Proceedings

Abstract
It is recognised that the imbalanced data problem is aggravated by other difficulty factors, such as class overlap. Over the years, several research works have focused on this problematic, although presenting two major hitches: the limitation of test domains and the lack of a formulation of the overlap degree, which makes results hard to generalise. This work studies the performance degradation of classifiers with distinct learning biases in overlap and imbalanced contexts, focusing on the characteristics of the test domains (shape, dimensionality and imbalance ratio) and on to what extent our proposed overlapping measure (degOver) is aligned with the performance results observed. Our results show that MLP and CART classifiers are the most robust to high levels of class overlap, even for complex domains, and that KNN and linear SVM are the most aligned with degOver. Furthermore, we found that the dimensionality of data also plays an important role in explaining performance results. © Springer Nature Switzerland AG 2018.

2020

Correction to: Interpretable and Annotation-Efficient Learning for Medical Image Computing

Autores
Cardoso, JS; Nguyen, HV; Heller, N; Abreu, PH; Isgum, I; Silva, W; Cruz, R; Amorim, JP; Patel, V; Roysam, B; Zhou, SK; Jiang, SB; Le, N; Luu, K; Sznitman, R; Cheplygina, V; Mateus, D; Trucco, E; Sureshjani, SA;

Publicação
Interpretable and Annotation-Efficient Learning for Medical Image Computing - Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings

Abstract

2020

Reviewing Autoencoders for Missing Data Imputation: Technical Trends, Applications and Outcomes

Autores
Pereira, RC; Santos, MS; Rodrigues, PP; Abreu, PH;

Publicação
J. Artif. Intell. Res.

Abstract

2020

VAE-BRIDGE: Variational Autoencoder Filter for Bayesian Ridge Imputation of Missing Data

Autores
Pereira, RC; Abreu, PH; Rodrigues, PP;

Publicação
2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)

Abstract
The missing data issue is often found in real-world datasets and it is usually handled with imputation strategies that replace the missing values with new data. Recently, generative models such as Variational Autoencoders have been applied for this imputation task. However, they were always used to perform the entire imputation, which has presented limited results when comparing to other state-of-the-art methods. In this work, a new approach called Variational Autoencoder Filter for Bayesian Ridge Imputation is introduced. It uses a Variational Autoencoder at the beginning of the imputation pipeline to filter the instances that are later fitted to a Bayesian ridge regression used to predict the new values. The approach was compared to four state-of-the-art imputation methods using 10 datasets from the healthcare context covering clinical trials, all injected with missing values under different rates. The proposed approach significantly outperformed the remaining methods in all settings, achieving an overall improvement between 26% and 67%.

2019

MNAR Imputation with Distributed Healthcare Data

Autores
Pereira, RC; Santos, MS; Rodrigues, PP; Abreu, PH;

Publicação
PROGRESS IN ARTIFICIAL INTELLIGENCE, PT II

Abstract
Missing data is a problem found in real-world datasets that has a considerable impact on the learning process of classifiers. Although extensive work has been done in this field, the MNAR mechanism still remains a challenge for the existing imputation methods, mainly because it is not related with any observed information. Focusing on healthcare contexts, MNAR is present in multiple scenarios such as clinical trials where the participants may be quitting the study for reasons related to the outcome that is being measured. This work proposes an approach that uses different sources of information from the same healthcare context to improve the imputation quality and classification performance for datasets with missing data under MNAR. The experiment was performed with several databases from the medical context and the results show that the use of multiple sources of data has a positive impact in the imputation error and classification performance. © 2019, Springer Nature Switzerland AG.

2020

Missing Image Data Imputation using Variational Autoencoders with Weighted Loss

Autores
Pereira, RC; Santos, JC; Amorim, JP; Rodrigues, PP; Abreu, PH;

Publicação
28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020, Bruges, Belgium, October 2-4, 2020

Abstract
Missing data is an issue often addressed with imputation strategies that replace the missing values with plausible ones. A trend in these strategies is the use of generative models, one being Variational Autoencoders. However, the default loss function of this method gives the same importance to all data, while a more suitable solution should focus on the missing values. In this work an extension of this method with a custom loss function is introduced (Variational Autoencoder with Weighted Loss). The method was compared with state-of-the-art generative models and the results showed improvements higher than 40% in several settings. © ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

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