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Modeling Dynamic Systems

Members:

Responsible: Luis Torgo

Summary/Objectives:

This area addresses the task of inducing models of complex dynamic systems based on historical records of data related to their functioning. The systems we are interested in modeling typically evolve in time and have clear non-stationary properties, demanding for the ability of handling regime shifts in the obtained models. Due to the complex nature of these systems as well as their different regimes of functioning we will also explore the use of different models in a collaborative way with the goal of modeling the system. We are particularly interested in understanding the conditions under which the systems exhibit extreme behavior, like for instance unusually high or low values of certain variables that describe the state of the systems. The detection and forecasting of this extreme behavior is of crucial importance in several application areas, like for instance finance, ecology, insurance, meteorology, etc.

Topics:

Ubiquous Data Mining

  • Spatio-temporal mining
  • Mining from network data

Mining Rare Events

  • Predicting extreme values
  • Outlier detection
  • Outlier ranking

Regression Analysis

  • Regression trees
  • Rule-based regression

Utility-based Learning

  • Utility-based regression
  • Evaluation of regression models

Publications:

  • Moniz, N, Torgo, L, Rodrigues, FFC, Resampling approaches to improve news importance prediction, Advances in Intelligent Data Analysis XIII - 13th International Symposium, IDA 2014, Leuven, Belgium, October 30 - November 1, 2014. Proceedings, Vol. 8819, pp. 12, 2014
  • Torgo, L, Ribeiro, RP, Pfahringer, B, Branco, P,SMOTE for regression, Progress in Artificial Intelligence - 16th Portuguese Conference on Artificial Intelligence, EPIA 2013, Angra do Heroísmo, Azores, Portugal, September 9-12, 2013. Proceedings, Vol. 8154 LNAI, pp. 12, 2013
  • Van Rijn, JN, Bischl, B, Torgo, L, Gao, B, Umaashankar, V, Fischer, S, Winter, P, Wiswedel, B, Berthold, MR, Vanschoren, J, OpenML: A collaborative science platform, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Vol. 8190 (PART 3), pp. 5, 2013
  • Joaquin Vanschoren, Jan N. Van Rijn, Bernd Bischl, Luis Torgo, OpenML: networked science in machine learning., SIGKDD Explorations, Vol. 15, pp. 12, 2013
  • Ohashi, O, Torgo, L, Spatial Interpolation using Multiple Regression, 12th IEEE International Conference on Data Mining (ICDM), pp. 6, 2012
  • Drury, B, Torgo, L, Almeida, JJ, Classifying news stories with a constrained learning strategy to estimate the direction of a market index, International Journal of Computer Science and Applications, Vol. 9 (1), pp. 22, 2012
  • Ohashi, O, Torgo, L, Wind speed forecasting using spatio-temporal indicators, ECAI 2012 - 20th European Conference on Artificial Intelligence. Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstrations Track, Montpellier, France, August 27-31 , 2012, Vol. 242, pp. 6, 2012
  • Drury, B, Torgo, L, Almeida, JJ, Classifying news stories to estimate the direction of a stock market index, Proceedings of the 6th Iberian Conference on Information Systems and Technologies, CISTI 2011, 2011
  • Torgo, L, Ohashi, O, 2D-interval predictions for time series, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, August 21-24, 2011, pp. 8, 2011
  • Drury, B, Dias, G, Torgo, L, A contextual classification strategy for polarity analysis of direct quotations from financial news, Recent Advances in Natural Language Processing, RANLP 2011, 12-14 September, 2011, Hissar, Bulgaria, pp. 7, 2011
  • Torgo, L, Lopes, E, Utility-based fraud detection, IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011, pp. 6, 2011
  • Herrera, M, Torgo, L, Izquierdo, J, Perez-Garcia, R, Predictive models for forecasting hourly urban water demand, Journal of Hydrology, Vol. 387 (1-2), pp. 10, 2010
  • Ohashi, O, Torgo, L, Ribeiro, RP, Interval Forecast of Water Quality Parameters, 19th European Conference on Artificial Intelligence (ECAI)/6th Conference on Prestigious Applications of Intelligent Systems (PAIS), Vol. 215, pp. 6, 2010
  • Torgo, L, Soares, C, Resource-bounded outlier detection using clustering methods, Frontiers in Artificial Intelligence and Applications, Vol. 218, pp. 15, 2010
  • Luís Torgo, Model Trees., Encyclopedia of Machine Learning, pp. 3, 2010
  • Luís Torgo, Regression Trees., Encyclopedia of Machine Learning, pp. 4, 2010


More details (incl. publications etc.)