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Details

  • Name

    Vítor Santos Costa
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st January 2009
007
Publications

2021

Evaluation Procedures for Forecasting with Spatiotemporal Data

Authors
Oliveira, M; Torgo, L; Costa, VS;

Publication
Mathematics

Abstract
The increasing use of sensor networks has led to an ever larger number of available spatiotemporal datasets. Forecasting applications using this type of data are frequently motivated by important domains such as environmental monitoring. Being able to properly assess the performance of different forecasting approaches is fundamental to achieve progress. However, traditional performance estimation procedures, such as cross-validation, face challenges due to the implicit dependence between observations in spatiotemporal datasets. In this paper, we empirically compare several variants of cross-validation (CV) and out-of-sample (OOS) performance estimation procedures, using both artificially generated and real-world spatiotemporal datasets. Our results show both CV and OOS reporting useful estimates, but they suggest that blocking data in space and/or in time may be useful in mitigating CV’s bias to underestimate error. Overall, our study shows the importance of considering data dependencies when estimating the performance of spatiotemporal forecasting models.

2021

Predictive Maintenance for Sensor Enhancement in Industry 4.0

Authors
Silva, C; da Silva, MF; Rodrigues, A; Silva, J; Costa, VS; Jorge, A; Dutra, I;

Publication
Recent Challenges in Intelligent Information and Database Systems - Communications in Computer and Information Science

Abstract

2021

Biased resampling strategies for imbalanced spatio-temporal forecasting

Authors
Oliveira, M; Moniz, N; Torgo, L; Costa, VS;

Publication
INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS

Abstract
Extreme and rare events, such as spikes in air pollution or abnormal weather conditions, can have serious repercussions. Many of these sorts of events develop through spatio-temporal processes. Timely and accurate predictions are a most valuable tool in addressing their impact. We propose a new set of resampling strategies for imbalanced spatio-temporal forecasting tasks, which introduce bias into formerly random processes. This bias is a combination of a spatial and a temporal weight, which can be either static or relevance-aware, and includes a hyper-parameter that regulates the relative importance of the temporal and spatial dimensions in the selection of observations during under- or over-sampling. We test and compare our proposals against standard versions of the strategies on 10 different geo-referenced numeric time series, using 3 distinct off-the-shelf learning algorithms. Experimental results show that our proposals provide an advantage over random resampling strategies in imbalanced numerical spatio-temporal forecasting tasks.

2021

SicknessMiner: a deep-learning-driven text-mining tool to abridge disease-disease associations

Authors
Rosario Ferreira, N; Guimaraes, V; Costa, VS; Moreira, IS;

Publication
BMC BIOINFORMATICS

Abstract
Background Blood cancers (BCs) are responsible for over 720 K yearly deaths worldwide. Their prevalence and mortality-rate uphold the relevance of research related to BCs. Despite the availability of different resources establishing Disease-Disease Associations (DDAs), the knowledge is scattered and not accessible in a straightforward way to the scientific community. Here, we propose SicknessMiner, a biomedical Text-Mining (TM) approach towards the centralization of DDAs. Our methodology encompasses Named Entity Recognition (NER) and Named Entity Normalization (NEN) steps, and the DDAs retrieved were compared to the DisGeNET resource for qualitative and quantitative comparison. Results We obtained the DDAs via co-mention using our SicknessMiner or gene- or variant-disease similarity on DisGeNET. SicknessMiner was able to retrieve around 92% of the DisGeNET results and nearly 15% of the SicknessMiner results were specific to our pipeline. Conclusions SicknessMiner is a valuable tool to extract disease-disease relationship from RAW input corpus.

2021

The Treasury Chest of Text Mining: Piling Available Resources for Powerful Biomedical Text Mining

Authors
Rosário-Ferreira, N; Marques-Pereira, C; Pires, M; Ramalhão, D; Pereira, N; Guimarães, V; Santos Costa, V; Moreira, IS;

Publication
BioChem

Abstract
Text mining (TM) is a semi-automatized, multi-step process, able to turn unstructured into structured data. TM relevance has increased upon machine learning (ML) and deep learning (DL) algorithms’ application in its various steps. When applied to biomedical literature, text mining is named biomedical text mining and its specificity lies in both the type of analyzed documents and the language and concepts retrieved. The array of documents that can be used ranges from scientific literature to patents or clinical data, and the biomedical concepts often include, despite not being limited to genes, proteins, drugs, and diseases. This review aims to gather the leading tools for biomedical TM, summarily describing and systematizing them. We also surveyed several resources to compile the most valuable ones for each category.

Supervised
thesis

2021

Overcoming the current limitations of Reinforcement Learning towards Artificial General Intelligence

Author
Filipe Emanuel dos Santos Marinho da Rocha

Institution
UP-FCUP

2021

Advising Diabetes’ self-management supported by user data in a mobile platform

Author
Diogo Roberto de Melo e Diogo Machado

Institution
UP-FCUP

2021

Type Assignment in Logic Programming

Author
João Luis Alves Barbosa

Institution
UP-FCUP

2021

Predictive Analytics for Spatio-Temporal Data

Author
Mariana Rafaela Figueiredo Ferreira de Oliveira

Institution
UP-FCUP

2021

NeuralLog: A Neural Logic System for Parameter and Structure Learning

Author
Victor Augusto Lopes Guimarães

Institution
UP-FCUP