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Details

  • Name

    Vítor Santos Costa
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st January 2009
006
Publications

2019

Contrasting logical sequences in multi-relational learning

Authors
Ferreira, CA; Gama, J; Costa, VS;

Publication
Progress in Artificial Intelligence

Abstract
In this paper, we present the BeamSouL sequence miner that finds sequences of logical atoms. This algorithm uses a levelwise hybrid search strategy to find a subset of contrasting logical sequences available in a SeqLog database. The hybrid search strategy runs an exhaustive search, in the first phase, followed by a beam search strategy. In the beam search phase, the algorithm uses the confidence metric to select the top k sequential patterns that will be specialized in the next level. Moreover, we develop a first-order logic classification framework that uses predicate invention technique to include the BeamSouL findings in the learning process. We evaluate the performance of our proposals using four multi-relational databases. The results are promising, and the BeamSouL algorithm can be more than one order of magnitude faster than the baseline and can find long and highly discriminative contrasting sequences. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

2019

Biased Resampling Strategies for Imbalanced Spatio-Temporal Forecasting

Authors
Oliveira, M; Moniz, N; Torgo, L; Santos Costa, V;

Publication
2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

Abstract

2018

Evaluation Procedures for Forecasting with Spatio-Temporal Data

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

Publication
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part I

Abstract

2017

Managing Diabetes: Counselling Supported by User Data in a Mobile Platform

Authors
Machado, D; Dutra, I; Brandão, P; Costa, VS;

Publication
Proceedings of the Doctoral Consortium, Challenge, Industry Track, Tutorials and Posters @ RuleML+RR 2017 hosted by International Joint Conference on Rules and Reasoning 2017 (RuleML+RR 2017), London, UK, July 11-15, 2017.

Abstract
Diabetes management is a complex problem. The patient needs to monitor several parameters in order to react in the most appropriate way. Different situations require the diabetic to understand and evaluate different rules. The main source of knowledge for those rules arises from medical practice and is usually transmitted through medical appointments. Given this initial advice, most patient are on a continuous process of managing the disease, toward achieving the best possible quality of life. Motivated by recent aadvances in diabetes monitoring devices, we introduce a diabetes support system designed to accompany the user, advising her and providing early guidance to avoid some of the many complications associated with diabetes. To accomplish this goal, we incorporate standard medical protocols, advice and directives in a Rule Based System (RBS). This RBS which we call Advice Rule Based System (ARBS) is capable of advising and uncovering possible causes for different occurrences. We believe that this solution is not only beneficial to the patient, but may also may be of use to the clinitians advising the patient. The device has continuous contact with the patient, thus it can provide early response if/where needed, Moreover, the system can provide useful data, that an authorized medical expert can use while prescribing a particular treatment, or even when investingating this health problem. We have started to add data-mining algorithms and methods, to uncover hidden behavioural patterns that may lead to crisis situations. Ultimately, through refining the rule systems base don human and machine learning, our approach has the potential for personalising the system according to the habits and phenotype of its user. The system is to be incorporated in a currently developed diabetes management application for Android.

2017

Managing diabetes: Pattern discovery and counselling supported by user data in a mobile platform

Authors
Machado, D; Paiva, T; Dutra, I; Costa, VS; Brandao, P;

Publication
2017 IEEE Symposium on Computers and Communications, ISCC 2017, Heraklion, Greece, July 3-6, 2017

Abstract
Diabetes management is a complex and a sensible problem as each diabetic is a unique case with particular needs. The optimal solution would be a constant monitoring of the diabetic's values and automatically acting accordingly. We propose an approach that guides the user and analyses the data gathered to give individual advice. By using data mining algorithms and methods, we uncover hidden behaviour patterns that may lead to crisis situations. These patterns can then be transformed into logical rules, able to trigger in a particular context, and advise the user. We believe that this solution, is not only beneficial for the diabetic, but also for the doctor accompanying the situation. The advice and rules are useful input that the medical expert can use while prescribing a particular treatment. During the data gathering phase, when the number of records is not enough to attain useful conclusions, a base set of logical rules, defined from medical protocols, directives and/or advice, is responsible for advise and guiding the user. The proposed system will accompany the user at start with generic advice, and with constant learning, advise the user more specifically. We discuss this approach describing the architecture of the system, its base rules and data mining component. The system is to be incorporated in a currently developed diabetes management application for Android. © 2017 IEEE.

Supervised
thesis

2019

Exascale computing with custom Linear Mixed Model kernels and GPU accelerators for Genomic Wide Association Studies and personalized medicine

Author
Christopher David Harrison

Institution
UP-FCUP

2019

Overcoming the current limitations of Reinforcement Learning towards Artificial General Intelligence

Author
Filipe Emanuel dos Santos Marinho da Rocha

Institution
UP-FCUP

2019

Probabilistic Logic-based Models For Gene Regulatory Networks

Author
António José Santos Freitas Gonçalves

Institution
UP-FCUP

2019

Type Assignment in Logic Programming

Author
João Luis Alves Barbosa

Institution
UP-FCUP

2019

NeuralLog: a Neural Logic Language

Author
Victor Augusto Lopes Guimarães

Institution
UP-FCUP