4 Research Activities

This section presents my main research activities that may help in understanding the impact of my research. Assessing scientific quality is not an easy task and it is still source of debate among many scholars. One of the key issues on this assessment is typically the publication record and associated bibliometric numbers. However, there are many disciplines where other aspects of research activity can be regarded as significant in terms of the impact on the community, and that is frequently the case of Computer Science, the field were I belong. For instance, developping a software tool that is widely used by the community and allows for further advances may well be more important than many publications and yet it is seldom considered as a criterion as it is not easy to assess without being involved in the concrete field of expertise. In my personal case I have developped many software programs that are widely used by the community, like for instance several R packages that have hundreds of monthly downloads. I have also created and still mantain a free regression data set repository that is used in most papers that test new regression algorithms whithin the machine learning community. This type of contributions, although relevant and important in my personal opinion, are hardly reflected in any type of research bliblometrics (with the exception of the data set repository whose citations are counted by Google Scholar although not all papers that use these data sets, cite the repository).

In spite of the previously mentioned limitations I have collected some data concerning bibliometric information related with my publications with the goal of helping in assessing the impact of this aspect of my research activities. The inclusion of this information requires some clarifications. Bibliometric data involves two main decisions: (i) whether to use it or not; and (ii) if yes, from which source. The first decision is hard to escape - it is the norm nowadays to evaluate CVs based on these numbers. Unfortunately this process often leads to unwanted bad decisions. There is a growing awareness of the research community that these numbers may be seriously misleading, particularly in some disciplines (like Computer Science) where the publication culture is rather different from other disciplines, for instance in the case of international conferences. While in most disciplines works published in conferences are not subject to peer reviewing, that is not the case in CS where international conferences are always peer reviewed and some are far more competitive than most journals. This leads to a clear bias on some of the frequently used bibliometric indices that frequently disregard conferences. Regarding this issue of the bias and risk of misjudgment of bibliometrics it may be interesting to point out several very interesting recent papers on this topic:

  • Diana Hicks, Paul Wouters, Ludo Waltman, Sarah de Rijcke & Ismael Rafols.Bibliometrics: The Leiden Manifesto for research metrics. Nature 520, 429–431 (23 April 2015) doi:10.1038/520429a
  • Paula Stephan, Reinhilde Veugelers & Jian Wang. Reviewers are blinkered by bibliometrics. Nature 544, 411–412 (27 April 2017) doi:10.1038/544411a
  • Rinze Benedictus, Frank Miedema & Mark W. J. Ferguson. Fewer numbers, better science. Nature 538, 453–455 (27 October 2016) doi:10.1038/538453a

These and many other works have been trying to raise the awareness of the community for the danger of these bibliometric-based decisions. Still, as this is still the norm I will present some of these numbers with the list of publications given below.

The second decision concerns the source of the bibliometric numbers. I have considered 3 sources: Thomson ISI, Elsevier Scopus and Google Scholar. All of them have potential drawbacks. Still, the decision was to select Google Scholar (GS). This index overcomes some limitations the other indices have, particularly for Computer Science, as it is clearly much more inclusive, although with an increased risk of inflating numbers. A few examples of my personal CV provide illustrations of the problems. My top cited publication is my book Data Mining with R published by a major publishing house (CRC Press from Taylor and Francis). For some reason that I cannot explain but most probably related with financial issues between the companies involved, this book is not indexed by Scopus. So, a peer reviewed book by a major publisher is non-existent for this widely used bibliometric index; (ii) one of my recent journal publications is on ACM Computing Surveys, one of the top journals of Computer Science according to the 2017 data from Thomson ISI impact factor. At the time of writing (12/Dec/2017), according to Google Scholar (GS) this publication from 2016 has 42 citations. According to Scopus this citation number is 20! If we check the concrete citations which is possible on GS we can observe that 6 of these are self-citations, so we can reduce this to 36, but this is still too far from 20. These and other similar effects lead to divergent values like the fact that my h-index according to Scopus is 13, while GS says it is 22, a very common phenomenon for computer science researchers as mentioned in [^1]. Still, GS is not without problems either. For instance, the same book I’ve mentioned above has two quite different editions currently, the second being more than 150 pages longer, with the other pages having been significantly revised. In spite of this GS insists in not considering these two books which obviously has an impact on the bibliometrics (I’ve inserted it manually but citations are not being counted by GS).

[^1] - Judit Bar-Ilan. Which h-index? — A comparison of WoS, Scopus and Google Scholar. Scientometrics, Volume 74, Issue 2, pp. 257–271.

Summarizing, I have decided to include bibliometric data from Google Scholar, even-though I’m well aware that this is a source of debate but my decision was essentially guided by completeness criteria both in terms of computer science in general and in my particular case. Moreover, in order to try to provide more information on the quality of the peer-reviewed conference papers, I have added the CORE rank of the respective conferences. CORE is a widely used source of information on the computer science conferences reputation. It is used wordwide by many funding agencies and universities where the specificities of the computer science field have been recognised. This austrolasian association provides a rank for international conferences using a scale from A* till Unranked. Further details on the criteria and meaning of this classification schema can be obtained here.

4.1 Publications

Global bibliometrics

Important Note: All citation numbers were obtained from my Google Scholar profile on 2018-01-11.

Global scores:

Total Nr. of Citations h-index i10-index
2211 23 41

Yearly evolution of the number of citations:

Books

[1] L. Torgo. Data Mining with R: Learning with Case Studies, Second Edition. Chapman and Hall/CRC, 2017.
(document) (extra information)

[2] L. Torgo. Data Mining with R: Learning with Case Studies (Chinese Edition). China Machine Press, 2012.
(document)

[3] L. Torgo. Data Mining with R: Learning with Case Studies. Chapman and Hall/CRC Data Mining and Knowledge Discovery Series. CRC Press, 2010.
(311 citations) (document) (extra information)

[4] L. Torgo. A Linguagem R, programação para a análise de dados. Escolar Editora, 2009.
(document)

Edition of Books

[1] A. Jorge, L. Torgo, P. Brazdil, et al., ed. Knowledge Discovery in Databases: PKDD 2005: 9th European Conference on Principles and Practice of Knowledge Discovery in Databases. LNAI 3721. Springer, 2005.
(3 citations)

[2] J. Gama, R. Camacho, P. Brazdil, et al., ed. Machine Learning: ECML 2005: 16th European Conference on Machine Learning. LNAI 3720. Springer, 2005.
(5 citations)

Chapters in Books

[1] L. Torgo. “Regression Trees”. In: Encyclopedia of Machine Learning and Data Mining. Ed. by C. Sammut and G. I. Webb. Springer, 2016, pp. 1080-1083.
(document)

[2] L. Torgo. “Model Trees”. In: Encyclopedia of Machine Learning and Data Mining. Ed. by C. Sammut and G. I. Webb. Springer, 2016, pp. 845-843.
(document)

[3] L. Torgo. “Regression Trees”. In: Encyclopedia of Machine Learning. Ed. by C. Sammut and G. I. Webb. Springer, 2011, pp. 842-845.

[4] L. Torgo. “Model Trees”. In: Encyclopedia of Machine Learning. Ed. by C. Sammut and G. I. Webb. Springer, 2011, pp. 684-686.

[5] L. Torgo and C. Soares. “Resource-bounded Outlier Detection Using Clustering Methods”. In: Data Mining for Business Applications. Ed. by C. Soares and R. Ghani. Frontiers in Artificial Intelligence and Applications. IOS Press, 2010, pp. 84-98.
(8 citations)

[6] P. Flach, H. Blockeel, T. Gartner, et al. “Data Mining and Decision Support, Integration and Collaboration”. In: On the road to knowledge: mining 21 years of UK traffic accident reports. Ed. by D. Mladenic, N. Lavrac, M. Bohanec and S. Moyle. Morgan Kaufmann, 2003, pp. 143-156.
(11 citations)

[7] T. Hellström and L.Torgo. “Post processing trading signals for improved trading performance”. In: Data Mining III. WIT Press, 2002, pp. 437-447.
(document)

[8] P. Brazdil and L. Torgo. “Knowledge Acquisition via Knowledge Integration”. In: Current Trends in Knowledge Acquisition. Ed. by B. e. a. Wielinga. IOS Press, 1990, pp. 90-104.
(79 citations) (document)

Journals

[1] L. Torgo. “Uma Breve Introdução à Data Science (in portuguese)”. In: Boletim da APDIO 57 (2017), pp. 9-11.
(document)

[2] M. Monteiro, J. Séneca, L. Torgo, et al. “Environmental controls on estuarine nitrifying communities along a salinity gradient”. In: Aquatic Microbial Ecology 80 (2) (2017), pp. 167-180.
(document)

[3] N. Moniz, L. Torgo, M. Eirinaki, et al. “A Framework for Recommendation of Highly Popular News Lacking Social Feedback”. In: New Generation Computing 35 (4) (2017), pp. 417-450.
(document)

[4] N. Moniz, P. Branco and L. Torgo. “Resampling Strategies for Imbalanced Time Series Forecasting”. In: International Journal of Data Science and Analytics 3.3 (2017), pp. 161-181.
(document)

[5] N. Moniz, L. Torgo and J. Vinagre. “Data-driven relevance judgments for ranking evaluation”. In: CoRR abs/1612.06136 (2016).
(document)

[6] L. Baía and L. Torgo. “A comparative study of approaches to forecast the correct trading actions”. In: Expert Systems 34.1 (2016), pp. e12169-n/a.
(1 citations) (document)

[7] P. Branco, L. Torgo and R. Ribeiro. “A Survey of Predictive Modeling on Imbalanced Domains”. In: ACM Comput. Surv. 49.2-31 (2016).
(47 citations) (document)

[8] P. Branco, R. Ribeiro and L. Torgo. “A UBL: an R package for Utility-based Learning”. In: CoRR abs/1604.08079 (2016).
(8 citations) (document)

[9] N. Moniz and L. Torgo. “Socially Driven News Recommendation”. In: CoRR abs/1506.01743 (2015).
(document)

[10] P. Branco, L. Torgo and R. Ribeiro. “A Survey of Predictive Modelling under Imbalanced Distributions”. In: CoRR abs/1505.01658 (2015).
(13 citations) (document)

[11] L. Torgo, P. Branco, R. P. Ribeiro, et al. “Re-sampling Strategies for Regression”. In: Expert Systems 32.3 (2015), pp. 465-476.
(28 citations) (document)

[12] L. Torgo. “An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models in R”. In: CoRR abs/1412.0436 (2014).
(18 citations) (document) (extra information)

[13] J. Vanschoren, J. N. van Rijn, B. Bischl, et al. “OpenML: networked science in machine learning”. In: SIGKDD Explorations Newsletter 15.2 (2013), pp. 49-60.
(117 citations) (document)

[14] B. Drury, L. Torgo and J. J. Almeida. “Classifying News Stories with a Constrained Learning Strategy to Estimate the Direction of a Market Index”. In: IJCSA 9.1 (2012), pp. 1-22.
(10 citations)

[15] M. Herrera, L. Torgo, J. Izquierdo, et al. “Predictive models for forecasting hourly urban water demand”. In: Journal of Hydrology 387.1-2 (Jun. 2010), pp. 141-150.
(208 citations) (document)

[16] L. Torgo and R. P. Ribeiro. “Modelos de Previsão de Valores Extremos e Raros”. In: Boletim da Sociedade Portuguesa de Estat'istica Primavera 2010 (2010), pp. 15-22.

[17] L. Torgo. “Deteção de fraude usando o R: um caso de estudo”. In: Boletim da Sociedade Portuguesa de Estatística (2009).

[18] R. Ribeiro and L. Torgo. “A Comparative Study on Predicting Algae Blooms in Douro River, Portugal”. In: Ecological Modelling - Selected Papers from the 5th European Conference on Ecological Modelling 212.1-2 (2008), pp. 86-91.
(17 citations) (document)

[19] A. Silva, A. Jorge and L. Torgo. “Design of an end-to-end method to extract information from tables”. In: International Journal on Document Analysis and Recognition 8.2-3 (2006), pp. 144-171.
(67 citations)

[20] L. Torgo and J. P. Costa. “Clustered Partial Linear Regression”. In: Machine Learning 50.3 (2003), pp. 303-319.
(10 citations) (document)

[21] L. Torgo. “Thesis: Inductive learning to tree-based regression models.”. In: AI Commun. 13.2 (2000), pp. 137-138.

[22] L. Torgo and J. Gama. “Regression using Classification Algorithms”. In: Intelligent Data Analysis 1.4 (1997).
(47 citations) (document)

Full Papers at International Conferences with Peer Reviewing

[1] V. Cerqueira, L. Torgo, M. Oliveira, et al. “Dynamic and Heterogeneous Ensembles for Time Series Forecasting”. In: Proceedings of DSAA 2017. 2017.
CORE rank: None

[2] P. Branco, L. Torgo, R. P. Ribeiro, et al. “Learning Through Utility Optimization in Regression Tasks”. In: Proceedings of DSAA 2017. 2017.
CORE rank: None

[3] V. Cerqueira, L. Torgo, J. Smailović, et al. “A Comparative Study of Performance Estimation Methods for Time Series Forecasting”. In: Proceedings of DSAA 2017. 2017.
CORE rank: None

[4] L. Torgo, B. Krawczyk, P. Branco, et al. “Learning with Imabalanced Domains: preface”. In: Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications. PMLR, 2017, pp. 1-6.

[5] P. Branco, L. Torgo and R. P. Ribeiro. “SMOGN: a Pre-processing Approach for Imbalanced Regression”. In: Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications. PMLR, 2017, pp. 36-50.

[6] N. Moniz, P. Branco and L. Torgo. “Evaluation of Ensemble Methods in Imbalanced Regression Tasks”. In: Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications. PMLR, 2017, pp. 129-140.

[7] V. Cerqueira, L. Torgo, F. Pinto, et al. “Arbitrated Ensemble for Time Series Forecasting”. In: Proceedings of the ECML/PKDD’2017 Conference. Ed. by M. Ceci, J. Hollmén, L. Todorovski and C. Vens. Lecture Notes in Artificial Intelligence. Springer, 2017, pp. 478-494.
CORE rank: A (BEST STUDENT MACHINE LEARNING PAPER)

[8] P. Branco, L. Torgo and R. P. Ribeiro. “Exploring Resampling with Neighborhood Bias on Imbalanced Regression Problems”. In: Proceedings of 18th EPIA Conference on Artificial Intelligence (EPIA 2017). Ed. by E. Oliveira, J. Gama, Z. Vale and H. L. Cardoso. LNCS (to appear). Springer, 2017, pp. 513-524.
CORE rank: B

[9] V. Cerqueira, L. Torgo and C. Soares. “Arbitrated Ensemble for Solar Radiation Forecasting”. In: Proceedings of IWANN’2017. Vol. 10305. LNCS. Springer, 2017, pp. 720-732.
CORE rank: B
(1 citations) (document)

[10] P. Branco, L. Torgo and R. P. Ribeiro. “Relevance-based Evaluation Metrics for Multi-class Imbalanced Domains”. In: Advances in Knowledge Discovery and Data Mining - 21th Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings. Ed. by J. Kim, K. Shim, L. Cao, J. Lee, X. Lin and Y. Moon. Lecture Notes in Computer Science, vol 10234. Springer. 2017, pp. 698-710.
CORE rank: A
(document)

[11] N. Guimarães, L. Torgo and A. Figueira. “Lexicon Expansion System for Domain and Time Oriented Sentiment Analysis”. In: Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. Vol. 1: KDIR. 2016.
CORE rank: C
(document)

[12] N. Moniz, L. Torgo and M. Eirinaki. “Time-Based Ensembles for Prediction of Rare Events in News Stream”. In: IEEE International Conference on Data Mining Workshops, ICDM Workshops 2016, December 12-15, 2016, Barcelona, Spain. 2016, pp. 1066-1073. DOI: 10.1109/ICDMW.2016.0154.
CORE rank: A*
(2 citations) (document)

[13] M. Oliveira, L. Torgo and V. S. Costa. “Predicting Wildfires: Propositional and Relational Spatio-Temporal Pre-Processing Approaches”. In: Proceedings of Discovery Science 2016. LNAI. Springer, 2016.
CORE rank: None
(document)

[14] N. Moniz, P. Branco and L. Torgo. “Resampling Strategies for Imbalanced Time Series”. In: Proceedings of DSAA 2016. 2016.
CORE rank: None
(3 citations) (document)

[15] A. Martins, A. Dias, E. Silva, et al. “MarinEye - A tool for marine monitoring”. In: OCEANS 2016 - Shanghai. 2016, pp. 1-7.
CORE rank: None
(document)

[16] L. Nezvaloví, L. Popelínsky, L. Torgo, et al. “Class-Based Outlier Detection: Staying Zombies or Awaiting for Resurrection?” In: Proceeedings of IDA’2015. Vol. 9385. Lecture Notes in Computer Science . Springer, 2015, pp. 193-204.
CORE rank: A
(1 citations) (document)

[17] L. Baia and L. Torgo. “Forecasting the Correct Trading Actions”. In: Proceeedings of 17th Portuguese Conference on Artificial Intelligence, EPIA 2015. LNAI. Springer, 2015, pp. 560-571.
CORE rank: B
(1 citations) (document)

[18] M. Oliveira and L. Torgo. “Ensembles for Time Series Forecasting”. In: Proceedings of Asian Conference on Machine Learning (ACML’2014). Vol. 39. JMLR: Workshop and Conference Proceedings. 2014, pp. 360-370.
CORE rank: None
(14 citations) (document) (extra information)

[19] N. Moniz, L. Torgo and F. Rodrigues. “Resampling approaches to improve news importance prediction”. In: Advances in Intelligent Data Analysis XIII (IDA’2014). Ed. by B. H.. Vol. 8819. LNCS. Springer, 2014, pp. 215-226.
CORE rank: A
(6 citations) (document)

[20] N. Moniz and L. Torgo. “Improvement of News Ranking through Importance Prediction”. In: Proceeding of KDD’2014 workshop NewsKDD - Data Science for News Publishing. 2014.
CORE rank: C
(2 citations) (document) (extra information)

[21] L. Torgo, R. P. Ribeiro, B. Pfahringer, et al. “SMOTE for Regression”. In: Proceedings of EPIA’2013. Springer, 2013.
CORE rank: B
(40 citations) (document) (extra information)

[22] O. Ohashi and L. Torgo. “Spatial Interpolation Using Multiple Regression”. In: Data Mining (ICDM), 2012 IEEE 12th International Conference on. 2012, pp. 1044-1049.
CORE rank: A*
(5 citations) (document) (extra information)

[23] O. Ohashi and L. Torgo. “Wind speed forecasting using spatio-temporal indicators”. In: ECAI 2012 - 20th European Conference on Artificial Intelligence. Ed. by L. D. Raedt, C. Bessière, D. Dubois, P. Doherty, P. Frasconi, F. Heintz and P. J. F. Lucas. IOS Press, 2012, pp. 975-980.
CORE rank: A
(19 citations) (document)

[24] B. Drury, G. Dias and L. Torgo. “A Contextual Classification Strategy for Polarity Analysis of Direct Quotations from Financial News”. In: RANLP. Ed. by G. Angelova, K. Bontcheva, R. Mitkov and N. Nicolov. RANLP 2011 Organising Committee, 2011, pp. 434-440.
CORE rank: C
(7 citations)

[25] B. Drury, L. Torgo and J. Almeida. “Classifying news stories to estimate the direction of a stock market index”. In: Information Systems and Technologies (CISTI), 2011 6th Iberian Conference on. 2011, pp. 1-4.
CORE rank: None
(11 citations) (document)

[26] B. Drury, L. Torgo and J. J. Almeida. “Guided Self Training for Sentiment Classification”. In: Proceedings of International Conference On Recent Advances in Natural Language Processing (RANLP 2011) - ROBUS workshop. RANLP 2011 Organising Committee, 2011.
CORE rank: C
(document)

[27] L. Torgo and E. Lopes. “Utility-Based Fraud Detection”. In: IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence. Ed. by T. Walsh. AAAI Press. IJCAI/AAAI, 2011, pp. 1517-1522.
CORE rank: A*
(5 citations) (document) (extra information)

[28] L. Torgo and O. Ohashi. “2D-interval predictions for time series”. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’2011. Ed. by C. A. é, J. Ghosh and P. Smyth. ACM, 2011, pp. 787-794.
CORE rank: A*
(5 citations) (document) (extra information)

[29] O. Ohashi, L. Torgo and R. P. Ribeiro. “Interval Forecast of Water Quality Parameters”. In: ECAI 2010 - 19th European Conference on Artificial Intelligence. Ed. by H. Coelho, R. Studer and M. Wooldridge. IOS Press, 2010, pp. 283-288.
CORE rank: A
(4 citations) (document)

[30] L. Torgo, W. Pereira and C. Soares. “Detecting Errors in Foreign Trade Transactions: Dealing with Insufficient Data”. In: 14th Portuguese Conference on Artificial Intelligence, EPIA 2009. Ed. by L. e. al Lopes. LNAI - 5816. Springer, 2009.
CORE rank: B
(2 citations) (document)

[31] L. Torgo and R. P. Ribeiro. “Precision and Recall for Regression.”. In: Discovery Science. Ed. by J. Gama, V. S. Costa, A. M. Jorge and P. Brazdil. Vol. 5808. Lecture Notes in Computer Science. Springer, 2009, pp. 332-346.
CORE rank: None
(26 citations) (document)

[32] R. Ribeiro and L. Torgo. “Utility-based performance measures for regression”. In: Proceedings of the 3rd Workshop on Evaluation Methods for Machine Learning, in conjunction with the 25th International Conference on Machine Learning (ICML 2008). 2008.
CORE rank: A*
(1 citations) (document)

[33] L. Torgo. “Resource-bounded Fraud Detection”. In: Progress In Artificial Intelligence, Proceedings of the 13th Portuguese Conference on Artificial Intelligence Workshops (EPIA 2007). Ed. by J. Neves, M. F. Santos and J. Machado. Vol. 4874. Lecture Notes in Artifical Intelligence. Springer, Dez. 2007, pp. 449-460.
CORE rank: None
(12 citations) (document)

[34] L. Torgo and R. Ribeiro. “Utility-based Regression”. In: Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2007). Ed. by K. JN, K. J. de Mántaras RL, S. Matwin, D. Mladenic and A. Skowron. Vol. 4702. Lecture Notes in Artificial Intelligence. Springer, 2007, pp. 597-604.
CORE rank: A
(36 citations) (document)

[35] R. Ribeiro and L. Torgo. “Rule-based Prediction of Rare Extreme Values”. In: Proceedings of the 9th International Conference on Discovery Science (DS’2006). LNAI. Springer, 2006.
CORE rank: None
(5 citations) (document)

[36] L. Torgo and R. Ribeiro. “Predicting Rare Extreme Values”. In: Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’2006). Ed. by W. Ng. Lecture Notes in Artificial Intelligence 3918. Springer, 2006.
CORE rank: A
(5 citations) (document)

[37] R. Ribeiro and L. Torgo. “A Comparative Study on Predicting Algae Blooms in River Douro, Portugal”. In: Proceedings of the V European Conference on Ecological Modelling (ECEM-2005). 2005.
CORE rank: None
(17 citations) (document)

[38] L. Torgo. “Regression Error Characteristic Surfaces”. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2005). Ed. by R. Grossman, R. Bayardo, K. Bennett and J. Vaidya. ACM Press, 2005, pp. 697-702.
CORE rank: A*
(22 citations) (document)

[39] L. Torgo. “The TNT Financial Trading System: a midterm report”. In: Proceedings of the Workshop on Data Mining for Business at ECML/PKDD 2005. 2005.
CORE rank: A
(1 citations) (document)

[40] L. Torgo and J. Marques. “Adapting Peepholing to Regression Trees”. In: Proceedings of the 12th EPIA. LNAI. Springer, 2005.
CORE rank: B
(document)

[41] A. Loureiro, L. Torgo and C. Soares. “Outlier Detection using Clustering Methods: a data cleaning application”. In: Proceedings of KDNet Symposium on Knowledge-based Systems for the Public Sector. 2004.
CORE rank: None
(145 citations) (document)

[42] R. P. Ribeiro and L. Torgo. “Predicting Harmful Algae Blooms.”. In: EPIA. Ed. by F. Moura-Pires and S. Abreu. Vol. 2902. Lecture Notes in Computer Science. Springer, 2003, pp. 308-312.
CORE rank: B
(8 citations) (document)

[43] A. C. e Silva, A. Jorge and L. Torgo. “Automatic Selection of Table Areas in Documents for Information Extraction.”. In: EPIA. Ed. by F. Moura-Pires and S. Abreu. Vol. 2902. Lecture Notes in Computer Science. Springer, 2003, pp. 460-465.
CORE rank: B
(3 citations)

[44] A. Silva, A. Jorge and L. Torgo. “Selection of Table Areas for Information Extraction”. In: Proceedings of the 3rd International Workshop in Document Analysis and its Applications (DLIA 2003). 2003.
CORE rank: None
(2 citations) (document)

[45] L. Torgo and R. Ribeiro. “Predicting Outliers”. In: Proceedings of Principles of Data Mining and Knowledge Discovery (PKDD’03). Ed. by N. Lavrac, D. Gamberger, L. Todorovski and H. Blockeel. 2838 LNAI. Springer, 2003, pp. 447-458.
CORE rank: A
(17 citations) (document)

[46] L. Torgo. “Computationally Efficient Linear Regression Trees”. In: Classification, Clustering and Data Analysis: recent advances and applications (Proc. of IFCS 2002). Ed. by K. Jajuga, A. Sokolowski and H. Bock. Studies in Classification, data analysis, and knowledge organization. Springer, 2002, pp. 409-415.
CORE rank: None
(11 citations) (document)

[47] P. Almeida and L. Torgo. “The Use of Domain Knowledge in Feature Construction for Financial Time Series Prediction”. In: Proceedings of the Portuguese AI Conference (EPIA’01). Ed. by P. Brazdil and A. Jorge. LNAI 2258. Springer, 2001, pp. 116-129.
CORE rank: B
(4 citations)

[48] L. Torgo. “A study on end-cut preference in least squares regression trees”. In: Proceedings of the Portuguese AI Conference (EPIA 2001). Ed. by P. Brazdil and A. Jorge. LNAI 2258. Springer, 2001, pp. 104-115.
CORE rank: B
(6 citations) (document)

[49] L. Torgo. “Efficient and Comprehensible Local Regression”. In: Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2000). Ed. by Terano, Liu and Chen. LNAI 1805. Springer, 2000, pp. 376-379.
CORE rank: A
(2 citations) (document) (extra information)

[50] L. Torgo. “Partial Linear Trees”. In: Proceedings of the 17th International Conference on Machine Learning (ICML 2000). Ed. by P. Langley. Morgan Kaufmann Publishers, 2000, pp. 1007-1014.
CORE rank: A*
(9 citations) (document)

[51] L. Torgo and J. P. Costa. “Clustered Multivariate Regression”. In: Data Analysis, Classification, and Related Methods. Ed. by Kiers, Rasson, Groenen and Schader. Springer, 2000.
CORE rank: None

[52] L. Torgo and J. P. Costa. “Clustered Partial Linear Regression”. In: Proceedings of the Fifth International Workshop on Multistrategy Learning (MSL-2000). Ed. by R. Michalski and P. Brazdil. 2000.
CORE rank: None
(10 citations) (document)

[53] L. Torgo and J. P. Costa. “Clustered Partial Linear Regression”. In: Proceedings of the 11th European Conference on Machine Learning (ECML 2000). Ed. by R. Mantaras and E. Plaza. LNAI 1810. Springer, 2000, pp. 426-436.
CORE rank: A
(9 citations)

[54] L. Torgo. “Predicting the Density of Algae Communities using Local Regression Trees”. In: Proceedings of the European Congress on Intelligent Techniques and Soft Computing (EUFIT’99). 1999.
CORE rank: None
(8 citations) (document)

[55] J. Gama, L. Torgo and C. Soares. “Dynamic Discretization of Continuous Attributes.”. In: IBERAMIA. Ed. by H. Coelho. Vol. 1484. Lecture Notes in Computer Science. Springer, 1998, pp. 160-169.
CORE rank: None
(40 citations)

[56] L. Torgo. “A Comparative Study of Reliable Error Estimators for Pruning Regression Trees”. In: Proceedings of the Iberoamericam Conference on AI (IBERAMIA-98). Ed. by H. Coelho. 1998.
CORE rank: None
(13 citations) (document)

[57] L. Torgo. “Error Estimates for Pruning Regression Trees”. In: Proceedings of the 10th European Conference on Machine Learning. Ed. by C. Nedellec and C. Rouveirol. LNAI 1398. Springer Verlag, 1998.
CORE rank: A
(16 citations) (document)

[58] L. Torgo. “Functional Models for Regression Tree Leaves”. In: Proceedings of the 14th International Conference on Machine Learning. Ed. by D. Fisher. Morgan Kaufmann Publishers, 1997.
CORE rank: A*
(135 citations) (document)

[59] L. Torgo. “Kernel Regression Trees”. In: Poster papers of the European Conference on Machine Learning (ECML-97). 1997.
CORE rank: A
(34 citations) (document)

[60] L. Torgo and J. Gama. “Search-based Class Discretization”. In: Proceedings of the European Conference on Machine Learning (ECML-97). LNAI 1224. Springer, 1997.
CORE rank: A
(29 citations) (document)

[61] L. Torgo and J. Gama. “Regression by Classification”. In: Advances In Artificial Intelligence, Proceedings of the 13th Brazilian Symposium on Artificial Intelligence (SBIA 1996). Ed. by D. L. Borges and C. A. A. Kaestner. Vol. 1159. Lecture Notes in Artificial Intelligence. Springer, Out. 1996, pp. 51-60.
CORE rank: None
(43 citations) (document)

[62] L. Torgo. “Applying Propositional Learning to Time Series Prediction”. In: Workshop on Statistics, Machine Learning and Knowledge Discovery in Databases. Ed. by Y. et all Kodratoff. 1995.
CORE rank: None
(6 citations) (document)

[63] L. Torgo. “Data Fitting with Rule-based Regression”. In: Proceedings of the 2nd International Workshop on Artificial Intelligence Techniques (AIT 1995). Ed. by J. Zizka and P. Brazdil. 1995.
CORE rank: None
(27 citations) (document)

[64] L. Torgo. “Controlled Redundancy in incremental Rule Learning”. In: European Conference on Machine Learning (ECML’93). Ed. by P. Brazdil. LNAI 667. Springer-Verlag, 1993, pp. 185-195.
CORE rank: A
(51 citations) (document)

[65] L. Torgo. “Rule Combination in Inductive Learning”. In: European Conference on Machine Learning (ECML’93). Ed. by P. Brazdil. LNAI 667. Springer-Verlag, 1993, pp. 384-389.
CORE rank: A
(14 citations) (document)

[66] P. Brazdil, M. Gams, S. Sian, et al. “Learning in Distributed Systems and Multi-Agent Environments”. In: Machine Learning: EWSL-91 (European Working Session on Learning). Ed. by Y. Kodratoff. Vol. 482. Lecture Notes in Artificial Intelligence. Springer, 1991, pp. 412-423.
CORE rank: None
(75 citations) (document)

[67] L. Torgo and M. Kubat. “Knowledge Integration and Forgetting”. In: Proceedings of the Czechoslovak Conference on Artificial Intelligence. 1991.
CORE rank: None
(4 citations) (document)

Other Publications

[1] C. Magalhães, A. Mucha, F. Carvalho, et al. “Development of an autonomous system for integrated marine monitoring”. In: Proceedings of 41st CIESM Congress. 2016.
(document)

[2] C. Magalhães, J. Séneca, C. Leite, et al. “Distribution and Environmental Controls on Marine Nitrogen Biogeochemical Functions”. In: Proceedings of 41st CIESM Congress. 2016.
(document)

[3] C. Magalhães, C. Lee, M. Monteiro, et al. “Everything is not everywhere: Antarctica Dry Valleys as an extreme counter example”. In: Proceedings of XXXIV SCAR. 2016.

[4] J. Séneca, C. Magalhães, M. Monteiro, et al. “Distribution of prokaryotic communities and NifH gene diversity in the extrem Darwin Mountains, Abtarctica”. In: Proceedings of XXXIV SCAR. 2016.

[5] M. Monteiro, J. Séneca, L. Torgo, et al. “The impact of environmental changes on nitrifyng communities from the Dry Valleys of Antarctica”. In: Proceedings of XXXIV SCAR. 2016.

[6] C. Bartilotti, A. Santos, R. Marques, et al. “Presenting the MarinEye project – Development and validation of a prototype for multitrophic oceanic monitoring”. In: Proceedings of ASC 2016- ICES Anual Science Meeting. 2016.

[7] C. Magalhães, A. Mucha, F. Carvalho, et al. “Development of an autonomous system for integrated marine monitoring”. In: Proceedings of XIX Iberian Symposium on Marine Biology Studies. 2016.
(document)

[8] L. Torgo. “Inductive Learning of Tree-based Regression Models”. PhD thesis. Faculty of Sciences, University of Porto, 1999.

4.2 Projects

Project Coordination

  • [2015 – 2018]
    Parfois Product/Shop Sales Forecasting for Supporting Logistics Decisions
    Private funding: Parfois - Principal investigator (PI)
    Budget: 100 kEur

  • [2015 – 2016]
    News Summarizer
    Private funding: SkimIT - Principal investigator (PI)
    Budget: 35 kEur

  • [2011 – 2014]
    e-Policy, Engineering the POlicy-making LIfe CYcle
    EC 7th Framework Programme Theme ICT-2011-7 (EC), Pr. Nr. 288147 - Local coordinator (PI of Portuguese Partner)
    Budget (local partner): 295 kEur; Global budget: 3 MEur

  • [2008 – 2011]
    MORWAQ, Monitoring and Predicting Water Quality Parameters
    Portuguese Science Foundation (FCT), PTDC/EIA/68489/2006 - Principal investigator (PI)
    Budget: 47 kEur

  • [2008 – 2011]
    oRANKI, Resource-bounded Outlier Detection
    Portuguese Science Foundation (FCT), PTDC/EIA/68322/2006 - Principal investigator (PI)
    Budget: 48,5 kEur

  • [2008]
    Exploratory Analysis of Sonae Distribuição Employees Survey (2008)
    MBA Consultores, private funding - Principal investigator (PI)

  • [2007 – 2008]
    Monitoring water quality parameters
    Águas do Douro e Paiva, SA, private funding - Principal investigator (PI)

  • [2004–2006]
    MODAL, Models for Predicting Algae Blooms in River Douro
    Portuguese Science Foundation (FCT), POSI/2000/SRI/40949 - Principal investigator (PI)
    Budget: 33 kEur

  • [2005]
    Exploratory Analysis of Sonae Distribuição Employees Survey (2005)
    MBA Consultores, private funding - Principal investigator (PI)

  • [2003 – 2004]
    Development of a System for Automatic Intraday Trading in Stock Markets
    private funding - Principal investigator (PI)

Project Participation

4.3 Prizes

  • [2017]
    Co-author (author was Vitor Cerqueira) of the paper that won the Best Student Machine Learning Paper Award given by the Machine Learning Journal at the European Conference on Machine Learnig (ECML/PKDD’2017)

  • [2006]
    Co-author (author was Rita Ribeiro) of the paper that won the Best Student Paper Award given by Yahoo! Research Labs at the Discovery Science (DS’06) international conference

  • [1999]
    Runner-up winner at the 3rd International Competition ``Protecting rivers and streams by monitoring chemical concentrations and algae communities’’, organized by ERUDIT in conjunction with COIL, the cluster of four European Research Networks (ERUDIT, EvoNet, MLNet and NeuroNet)

4.4 Thesis Supervision

Ph.D.’s

Ongoing

  • Paula Branco
    • Title: Utility-based Predictive Analytics
    • Supervisor: Luis Torgo; Co-Supervisor: Rita Ribeiro
    • MAPi PhD Program, Universities of Aveiro, Minho and Porto
    • Start: 2014
  • Mariana Oliveira
    • Title: Predictive Analytics for Dependent Data
    • Supervisor: Luis Torgo; Co-Supervisor: Vitor Santos Costa
    • MAPi PhD Program, Universities of Aveiro, Minho and Porto
    • Start: 2017
  • Vitor Cerqueira
    • Title: Ensembles for Time Series Forecasting
    • Supervisor: Luis Torgo; Co-Supervisor: Carlos Soares
    • PhD Program Faculty of Engineering/UPorto
    • Start: 2017
  • Nuno Guimarães
    • Title: Analyzing and Developing Veracity Indicators for Building an Automatic Detector of False Online News
    • Supervisor: Álvaro Figueira; co-supervisor: Luis Torgo
    • PhD on Computer Science, Faculty of Sciences, University of Porto
    • Start: 2017

Finished

  • Nuno Moniz
    • Title: Prediction and Ranking of Highly Popular Web Content
    • Supervisor: Luis Torgo
    • PhD on Computer Science, Faculty of Sciences, University of Porto
    • July/2017
    • Awarded the 2nd place in the Fraunhofer Portugal Challange
  • Brett Drury
  • Orlando Ohashi
  • Rita Ribeiro
    • Title: Utility-based Regression
    • Supervisor: Luis Torgo
    • PhD on Computer Science, Faculty of Sciences, University of Porto
    • September/2011
  • Pedro Almeida
    • Title: Previsão do Comportamento de Séries Temporais Financeiras com Apoio de Conhecimento Sobre o Domínio
    • Supervisor: Luis Torgo
    • Doutoramento em Engenharia Informática, Universidade da Beira Interior
    • April/2003

M.Sc.’s

Finished

  • Carlos Leite
    • Title: Domain Oriented Biclustering Validation
    • Supervisor: Luis Torgo; Co-Supervisor: Catarina Magalhães
    • Masters on Computer Science, Faculty of Sciences, University of Porto
    • 30/Nov/2016
    • Grade: 19 out of 20
  • Nuno Guimarães
  • Mariana Oliveira
  • Luís Baía
  • Paula Branco
  • Fernando Correia
  • João Cepêda
  • Pedro Coelho
  • Hélia Costa
  • Raquel Santos
    • Title: Modelos de Regressão para a Previsão de Vendas e de Clientes
    • Supervisor: Luis Torgo; Co-Supervisor: Luis Marques (SONAE)
    • Masters in Engineering Mathematics, Faculty of Sciences, University of Porto
    • 2010
  • Pedro Duarte
    • Title: Service-Oriented Architectures
    • Supervisor: Paulo Martins (Critical); Co-Supervisor: Luis Torgo
    • Masters in Network and Information Systems Engineering, Faculty of Sciences, University of Porto
    • 2010
  • Clara Gonçalves
    • Title: Modelos de Regressão com Análise Classificatória
    • Supervisor: Joaquim Pinto da Costa ; Co-Supervisor: Luis Torgo
    • Masters in Engineering Mathematics, Faculty of Sciences, University of Porto
    • 2005
  • Jorge Barbosa
    • Title: Métodos para lidar com Mudanças de Regime em Séries Temporais Financeiras
    • Supervisor: Luis Torgo
    • Master in Data Analysis and Decision Support Systems, Faculty of Economics, University of Porto
    • 2005
  • Joana Marques
    • Title: Um estudo sobre a eficiência computacional da construção de árvores de regressão
    • Supervisor: Luis Torgo
    • Masters in Artificial Intelligence and Computation, Faculty of Economics, University of Porto
    • 2004
  • Rita Ribeiro
    • Title: Modelos de Previsão de Fenómenos Raros
    • Supervisor: Luis Torgo
    • Masters in Artificial Intelligence and Computation, Faculty of Economics, University of Porto
    • 2003
  • Ana Silva
  • Mário Oldemiro
    • Title: Técnicas de Inteligência Artificial Aplicadas à Previsão de Séries Temporais Financeiras
    • Supervisor: Luis Torgo; Co-supervisor: Pavel Brazdil
    • Master in Data Analysis and Decision Support Systems, Faculty of Economics, University of Porto
    • 2002
  • César Rocha
  • Sílvia Amorim

4.5 Organization of Events

4.6 Scientific Reviewing

Academic Juris

  • [2017]
    Ph.D. Thesis of Pedro Saleiro - Entity-Specific Text Mining for Online Reputation Monitoring, University of Porto, Portugal

  • [2017]
    Ph.D. Thesis of Davi D’Andréa Baccan - Contributions of Computational Cognitive Modeling to the Understanding of the Financial Markets, University of Coimbra, Portugal

  • [2015]
    Ph.D. Thesis of Vinay Uday Prabhu - Network Aided Classification and Detection of Data, Carnegie Mellon University / MAP-I Doctoral Program, Pittsburgh, USA

  • [2013]
    Ph.D. Thesis of Ricardo Nuno Taborda Campos - Disambiguating Implicit Temporal Queries for Temporal Information Retrieval Applications, PhD on Computer Science, Faculty of Sciences, University of Porto, Portugal

  • [2012]
    Ph.D. Thesis of Nuno Constantino Castro - Time Series Motif Discovery, MAP-I Doctoral Program, University of Minho, Portugal

  • [2012]
    MSc. Thesis of Nuno Moniz - Bridging the gap between closed and open data, System proposal for the Portuguese Legislation, Masters on Computer Engineering, specialization in Networks, Architectures and Systems , ISEP, Portugal

  • [2011]
    Ph.D. Thesis of Rui Barbosa - Agents in the Market Place, University of Minho, Portugal

  • [2008]
    Ph.D. Thesis of Pedro Rafael de Ruiz Graça - Aprendizagem Interactiva em Sistemas Multi-Agente, University of Lisbon, Portugal

  • [2008]
    Ph.D. Thesis of Anneleen Van Assche - Improving the Applicability of Ensemble methods in Data Mining, Katholieke Universiteit Leuven, Belgium

  • [2007]
    Ph.D. Thesis of Pedro Gabriel Dias Ferreira - Sequence Pattern Mining in Biochemical Data, University of Minho, Portugal

  • [2005]
    Ph.D. Thesis of Kwok Pan Pang - Statistics for Structural Break Detection and Their Application to Forecasting and Statistical Process Control, Monash University, Australia

  • [2004]
    MSc. Thesis of Susana Pereira - Análise de Séries Temporais no Domínio das Telecomunicações Móveis, Masters on Statistics and Information Managment, ISEGI, New University of Lisbon, Portugal

  • [2003]
    Ph.D. Thesis of Vitor Lobo - Ship Noise Classification, a contribution to prototype based classifier design, New University of Lisbon, Portugal

  • [2003]
    MSc. Thesis of Raul Moisão - Modelo Predictivo, Baseado em Redes Neuronais, para Previsão em Séries Temporais com Origem em Sistemas Financeiros,New University of Lisbon, Portugal

Research projects

  • [2017]
    European Comission, Expert Reviewer (EX2017D300375), Review of 4 proposals for the H2020-MSCA-IF-2017 (Horizon 2020 Marie Sklodowska-Curie Actions - Individual Fellowships)

  • [2016]
    KU Leuven, Belgium. Review of one project proposal.

  • [2011 – 2014]
    FIRST - Large scale information extraction and integration infrastructure for supporting financial decision making. EC Seventh Framework Programme, project nr. 257928. Member of the Advisory Board.

  • [2013]
    Czech Science Foundation – GACR. Review of one project proposal.

  • [2011]
    Czech Science Foundation – GACR. Review of two projects proposals.

  • [2010]
    Czech Science Foundation – GACR. Review of one project proposal.

Journals

  • Data Mining and Knowledge Discovery, Springer.
    Reviewing of 8 submissions.

  • Journal of Machine Learning Research.
    Reviewing of 2 submissions.

  • Machine Learning Journal, Kluwer Academic Publishers.
    Reviewing of 11 submissions.

  • IEEE Transactions on Knowledge and Data Engineering, IEEE Computer Society.
    Reviewing of 2 submissions.

  • IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Computer Society.
    Reviewing of 1 submission.

  • Journal of Artificial Intelligence Research, Morgan Kaufmann.
    Reviewing of 1 submission.

  • Decision Support Systems, Elsevier.
    Reviewing of 2 submissions.

  • Neural Computing and Apllications, Springer.
    Reviewing of 1 submission.

  • Neural Networks, Elsevier.
    Reviewing of 1 submission.

  • Intelligent Data Analysis, Elsevier Science.
    Reviewing of 1 submission.

  • Expert Systems, Wiley.
    Reviewing of 1 submission.

  • International Journal of Human-Computer Studies, Elsevier Science.
    Reviewing of 1 submission.

  • AI Communications, IOS Press.
    Reviewing of 1 submission.

  • International Journal on Artificial Intelligence Tools, World Scientific.
    Reviewing of 2 submission.

International Conferences

  • KDD, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    2009 (PC member), 2007 (PC member)

  • ICML, International Conference on Machine Learning
    2016 (PC member), 2011 (PC member), 2010 (PC member), 2009 (Area Chair), 2008 (Area Chair), 2004 (PC member)

  • ECML, European Conference on Machine Learning
    2017 (Area Chair), 2016 (PC member), 2015 (Area Chair), 2014 (PC member), 2013 (PC member), 2012 (Area Chair), 2011 (Area Chair), 2010 (PC member), 2009 (PC member), 2008 (PC member), 2007 (Area Chair), 2006 (Area Chair), 2005 (Area Chair), 2004 (PC member), 2003 (PC member)

  • ICDM, IEEE International Conference on Data Mining
    2007 (PC member), 2006 (PC member), 2005 (PC member), 2004 (PC member)

  • PKDD, European Conference on Principles and Practice of Knowledge Discovery in Databases
    2017 (Area Chair), 2016 (PC member), 2015 (Area Chair), 2014 (PC member), 2013 (PC member), 2012 (Area Chair), 2011 (Area Chair), 2010 (PC member), 2009 (PC member), 2008 (PC member), 2007 (Area Chair), 2006 (Area Chair), 2005 (Program Chair), 2004 (PC member), 2003 (PC member)

  • PAKDD, Pacific-Asia Conference on Knowledge Discovery and Data Mining
    2009 (PC member), 2008 (PC member), 2007 (PC member)

  • DS, International Conference on Discovery Science
    2010 (PC member), 2009 (PC member), 2008 (PC member), 2007 (PC member)

  • AAAI, Conference on Artificial Intelligence
    2017 (PC member), 2015 (PC member), 2014 (PC member)

  • IJCAI, International Joint Conference on Artificial Intelligence
    2017 (PC member), 2013 (Senior PC member), 2011 (Senior PC member)

  • ECAI, European Conference on Artificial Intelligence
    2014 (Senior PC member), 2012 (PC member), 2010 (PC member)

  • NIPS, Annual Conference on Neural Information Processing Systems
    2014 (PC member)

  • ACML, Asian Conference on Machine Learning
    2012 (PC member)

  • UseR, The R User Conference
    2013 (PC member)

  • SAC, ACM Symposium on Applied Computing
    2005 (PC member)

  • ADMA, International Conference on Advanced Data Mining and Applications
    2008 (PC member), 2007 (PC member), 2006 (PC member), 2005 (PC member)

  • IBERAMIA, Iberoamerican Conference on Artificial Intelligence
    2002 (PC member), 2000 (PC member), 1998 (PC member)

  • EPIA, Portuguese AI Conference
    2005 (PC member), 2003 (PC member), 2001 (PC member)

  • SBIA, Brazilian Symposium on Artificial Intelligence
    2008 (PC member), 2004 (PC member)

  • ENIA, Brazilian Meeting on Artificial Intelligence
    2011 (PC member), 2007 (PC member)

4.7 Software

  • [2016]
    DMwR2 - an R package with functions and data for the 2nd edition of “Data Mining with R”
    GitHub project page

  • [2016, co-author]
    UBL - an R package for utility-based predictive analytics
    GitHub project page

  • [2014]
    performanceEstimation - an R package for estimating the performance of predictive models
    GitHub project page

  • [2010]
    DMwR - an R package with functions and data for the 1st edition of “Data Mining with R”

  • [2005]
    TNT - an autonomous trading system for financial markets

  • [2001]
    CLRT - Clustered regression models

  • [1999]
    RT - Tree-based regression models

  • [1997]
    C library for propositional learning (in conjuntion with João Gama)

  • [1996]
    RECLA - Regression through classification

  • [1996]
    KERTI - Kernel regression trees

  • [1996]
    EcoTerme - Calculus of thermic behaviour of buildings

  • [1995]
    R2 - regression rules learner

  • [1995]
    YAP-Prolog library for propositional learning

  • [1993]
    YAILS - Incremental learning of classification rules

  • [1991]
    INTEG - Knowledge integration system

4.8 Visits to Research Labs

  • [Jan/2017] One week visit to the Jozef Stefan Institute, Ljubljana, Slovenia. Host: Prof. Igor Mozetic

  • [Sep-Dec/2015]
    Four months visit to the Weka research lab at University of Waikato, New Zealand. Host: Prof. Bernhard Pfahringer.

  • [July/2012]
    Two weeks visit to the Text Analysis and Machine Learning (TAMALE) research lab at University of Ottawa, Canada. Host: Prof. Stan Matwin.

  • [May/2012]
    Two weeks visit to the Weka research lab at University of Waikato, New Zealand. Host: Prof. Bernhard Pfahringer.

  • [Jun/2010]
    One month visit to the Department of Informatics, University degli Studi di Bari, Italy. Host: Prof. Donato Malerba

  • [Fev-Jul/2008]
    Six months visit to the Weka research lab at University of Waikato, New Zealand. Host: Prof. Bernhard Pfahringer.

  • [Mar-Aug/2004]
    Five months visit to the Stern Business School of the University of New York. Hosts: Profs. Foster Provost and Vasant Dhar

  • [1994]
    Three months visit to the University of São Paulo, campus São Carlos, Brazil. Host: Prof. Carolina Monard

4.9 Invited Seminars

  • [July/2017] Data Pre-processing Methods for Forecasting with Spatio-Temporal Data, invited talk at the international conference Data Science, Statistics and Visualization, Lisbon, Portugal

  • [June/2017] Handling Imbalanced Regression Tasks through Utility- based Regression, invited seminar at Université de Fribourg, Fribourg, Switzerland

  • [May/2017] An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models in R, invited talk at SER, Niteroi, Rio de Janeiro, Brazil

  • [Jan/2017] Resampling Approaches for Handling Imbalanced Regression Tasks, Jozef Stefan Institute, Slovenia.

  • [Dec/2015]
    An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models in R, University of Waikato, New Zealand

  • [Sep/2015]
    Feature Engineering for Handling Spatial and Spatio-Temporal Forecasting, University of Waikato, New Zealand

  • [Jun/2015]
    The R Language - programming for data analysis, Join 2015, Braga, Portugal

  • [May/2015]
    Data Mining aplicado à Previsão de Blooms de Algas, Workshop Aquacultura 2015, Porto, Portugal

  • [May/2015]
    Data Science - what, why and how?, Porto Tech Hub, Porto, Portugal

  • [Mar/2015]
    An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models in R, Porto R Users Group (PRUG), Porto, Portugal

  • [Jan/2015]
    An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models in R, LIAAD Seminars, INESC Tec, Portugal

  • [Nov/2014]
    Monitoring and Forecasting Rare Events, Workshop INESC/CIIMAR, INESC Tec, Portugal

  • [Jun/2014]
    Dynamic Documents in R, DCC talks, FCUP/UPorto, Portugal

  • [Jan/2014]
    Spatio-temporal data mining and extreme behavior data mining, LIAAD Open-Day, INESC Tec, Portugal

  • [Jul/2013]
    Spatial Interpolation using Multiple Regression, University of Konstanz, Germany

  • [Feb/2013]
    Data Mining para a Deteção de Fraude, INESC Tec, Portugal

  • [May/2012]
    Modeling Deviations from Expected Behavior - two case studies, University of Waikato, New Zealand

  • [Out/2011]
    Modeling Deviations from Expected Behavior - two case studies, Back2Basics Seminar Series, Faculty of Engineering, University of Porto, Portugal

  • [Out/2011]
    Modeling Deviations from Expected Behavior - two case studies, Thought Leader Speaker Series, eBay Research Labs, San Jose, USA

  • [Jul/2011]
    Modelos de Previsão para Sistemas Dinâmicos Complexos, Seminários em Engenharia de Sistemas, University of Minho, Portugal

  • [Jun/2010]
    Resource-bounded Outlier Detection using Clustering Methods, Department of Informatics, University degli Studi di Bari, Italy.

  • [Jan/2009]
    Using Data Mining for Resource-aware Fraud Detection, Workshop on Data Mining for the Banking System, Faculty of Economics, University of Porto, Portugal

  • [Mar/2008]
    Utility-based Regression - recent developments, University of Waikato, New Zealand

  • [Jan/2008]
    Utility-based Regression - recent developments, Katholieke Universiteit Leuven, Belgium

  • [Jan/2007]
    Predicting Rare Extreme Values - recent developments, Solomon Seminars, Josef Stefan Institute, Slovenia

  • [Jan/2006]
    Non-Uniform Cost Surfaces for Predicting Rare Extreme Values, Solomon Seminars, Josef Stefan Institute, Slovenia

  • [Jan/2006]
    Regression Error Characteristic Surfaces, Solomon Seminars, Josef Stefan Institute, Slovenia

  • [Sep/2004]
    An autonomous trading system, International Summer School on Data Analysis, Instituto Superior de Gestão, Portugal

  • [Jan/2004]
    An intraday Autonomous Trading System, Faculty of Economics, University of Porto, Portugal

  • [Nov/2003]
    Mining DNA microarray data: techniques and applications, Instituto de Biologia Molecular e Celular, Porto, Portugal

  • [Jun/2003]
    Models for Predicting Water Quality, Jornadas em Informática (JOIN’03), University of Minho, Portugal

  • [Out/2002]
    Artificial Intelligence: from fiction to reality, 2 Ciclo de Conferências em Cibercultura, Guarda, Portugal

4.10 Service to the Community