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Publications

Publications by Luís Torgo

2016

MarinEye - A tool for marine monitoring

Authors
Martins, A; Dias, A; Silva, E; Ferreira, H; Dias, I; Almeida, JM; Torgo, L; Goncalves, M; Guedes, M; Dias, N; Jorge, P; Mucha, AP; Magalhaes, C; Carvalho, MDF; Ribeiro, H; Almeida, CMR; Azevedo, I; Ramos, S; Borges, T; Leandro, SM; Maranhao, P; Mouga, T; Gamboa, R; Lemos, M; dos Santos, A; Silva, A; Frazao e Teixeira, BFE; Bartilotti, C; Marques, R; Cotrim, S;

Publication
OCEANS 2016 - SHANGHAI

Abstract
This work presents an autonomous system for marine integrated physical-chemical and biological monitoring - the MarinEye system. It comprises a set of sensors providing diverse and relevant information for oceanic environment characterization and marine biology studies. It is constituted by a physical-chemical water properties sensor suite, a water filtration and sampling system for DNA collection, a plankton imaging system and biomass assessment acoustic system. The MarinEye system has onboard computational and logging capabilities allowing it either for autonomous operation or for integration in other marine observing systems (such as Observatories or robotic vehicles. It was designed in order to collect integrated multi-trophic monitoring data. The validation in operational environment on 3 marine observatories: RAIA, BerlengasWatch and Cascais on the coast of Portugal is also discussed.

2016

Time-Based Ensembles for Prediction of Rare Events In News Streams

Authors
Moniz, N; Torgo, L; Eirinaki, M;

Publication
2016 IEEE 16TH INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW)

Abstract
Thousands of news are published everyday reporting worldwide events. Most of these news obtain a low level of popularity and only a small set of events become highly popular in social media platforms. Predicting rare cases of highly popular news is not a trivial task due to shortcomings of standard learning approaches and evaluation metrics. So far, the standard task of predicting the popularity of news items has been tackled by either of two distinct strategies related to the publication time of news. The first strategy, a priori, is focused on predicting the popularity of news upon their publication when related social feedback is unavailable. The second strategy, a posteriori, is focused on predicting the popularity of news using related social feedback. However, both strategies present shortcomings related to data availability and time of prediction. To overcome such shortcomings, we propose a hybrid strategy of time-based ensembles using models from both strategies. Using news data from Google News and popularity data from Twitter, we show that the proposed ensembles significantly improve the early and accurate prediction of rare cases of highly popular news.

2017

Arbitrated Ensemble for Solar Radiation Forecasting

Authors
Cerqueira, V; Torgo, L; Soares, C;

Publication
Advances in Computational Intelligence - 14th International Work-Conference on Artificial Neural Networks, IWANN 2017, Cadiz, Spain, June 14-16, 2017, Proceedings, Part I

Abstract

2017

Relevance-Based Evaluation Metrics for Multi-class Imbalanced Domains

Authors
Branco, Paula; Torgo, Luis; Ribeiro, RitaP.;

Publication
Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings, Part I

Abstract
The class imbalance problem is a key issue that has received much attention. This attention has been mostly focused on two-classes problems. Fewer solutions exist for the multi-classes imbalance problem. From an evaluation point of view, the class imbalance problem is challenging because a non-uniform importance is assigned to the classes. In this paper, we propose a relevance-based evaluation framework that incorporates user preferences by allowing the assignment of differentiated importance values to each class. The presented solution is able to overcome difficulties detected in existing measures and increases discrimination capability. The proposed framework requires the assignment of a relevance score to the problem classes. To deal with cases where the user is not able to specify each class relevance, we describe three mechanisms to incorporate the existing domain knowledge into the relevance framework. These mechanisms differ in the amount of information available and assumptions made regarding the domain. They also allow the use of our framework in common settings of multi-class imbalanced problems with different levels of information available. © 2017, Springer International Publishing AG.

2017

A Framework for Recommendation of Highly Popular News Lacking Social Feedback

Authors
Moniz, N; Torgo, L; Eirinaki, M; Branco, P;

Publication
NEW GENERATION COMPUTING

Abstract
Social media is rapidly becoming the main source of news consumption for users, raising significant challenges to news aggregation and recommendation tasks. One of these challenges concerns the recommendation of very recent news. To tackle this problem, approaches to the prediction of news popularity have been proposed. In this paper, we study the task of predicting news popularity upon their publication, when social feedback is unavailable or scarce, and to use such predictions to produce news rankings. Unlike previous work, we focus on accurately predicting highly popular news. Such cases are rare, causing known issues for standard prediction models and evaluation metrics. To overcome such issues we propose the use of resampling strategies to bias learners towards these rare cases of highly popular news, and a utility-based framework for evaluating their performance. An experimental evaluation is performed using real-world data to test our proposal in distinct scenarios. Results show that our proposed approaches improve the ability of predicting and recommending highly popular news upon publication, in comparison to previous work.

2017

Exploring Resampling with Neighborhood Bias on Imbalanced Regression Problems

Authors
Branco, P; Torgo, L; Ribeiro, RP;

Publication
Progress in Artificial Intelligence - 18th EPIA Conference on Artificial Intelligence, EPIA 2017, Porto, Portugal, September 5-8, 2017, Proceedings

Abstract
Imbalanced domains are an important problem that arises in predictive tasks causing a loss in the performance of the most relevant cases for the user. This problem has been intensively studied for classification problems. Recently it was recognized that imbalanced domains occur in several other contexts and for a diversity of types of tasks. This paper focus on imbalanced regression tasks. Resampling strategies are among the most successful approaches to imbalanced domains. In this work we propose variants of existing resampling strategies that are able to take into account the information regarding the neighborhood of the examples. Instead of performing sampling uniformly, our proposals bias the strategies for reinforcing some regions of the data sets. In an extensive set of experiments we provide evidence of the advantage of introducing a neighborhood bias in the resampling strategies. © Springer International Publishing AG 2017.

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