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Algorithm Selection via Metalearning and Planning


PhD's involved: Pavel Brazdil (FEP, coord., LIAAD), João Gama (FEP, LIAAD)
Collaboration: Carlos Soares (FEUP, CESE), Rui Leite (FEP)
PhD students: Salisu Abdulrahman, Fábio Pinto (CESE).
MSc students: Miguel Cachada, Maria João Ferreira. 

Main objectives and activities:

  • Exploit metaknowledge / metalearning to aid the user in selecting the appropriate ML (or other) algorithm for a given problem.
  • Aid the user in selecting the appropriate combination of KDD / Data Mining methods for the given problem. Select from a range of data pre-processing and modelling methods.
  • Planning experiments to select an appropriate Data Mining method: Plan experiments so as to determine which algorithm has a better chance of producing better results.


  • P. Brazdil, Christophe G. Giraud-Carrier, C. Soares, R. Vilalta: Metalearning - Applications to Data Mining. Springer, 2009, 266 cit. (Google Scholar), ISBN 978-3-540-73262-4.

Articles with >100 citations (GS):

  • PB Brazdil, C Soares, JP Da Costa, Ranking learning algorithms: Using IBL and meta-learning on accuracy and time results, Machine Learning 50 (3), 251-277, 2003, 325 cit.
  • J Gama, P Brazdil, Characterization of classification algorithms, Progress in Artificial Intelligence, 189-200, 1995, 155 cit.
  • P Brazdil, J Gama, B Henery, Characterizing the applicability of classification algorithms using meta-level learning, Machine Learning: ECML-94, 83-102, 1994, 155 cit.
  • C Giraud-Carrier, R Vilalta, P Brazdil, Introduction to the special issue on meta-learning, Machine learning 54 (3), 187-193, 2004, 148 cit.
  • C Soares, PB Brazdil, P Kuba: A meta-learning method to select the kernel width in support vector regression, Machine Learning 54 (3), 195-209, 2004, 123 cit.
  • PB Brazdil, C Soares, A comparison of ranking methods for classification algorithm selection, Machine Learning: ECML 2000, 63-75, 2000,124 cit.


Other Selected Articles:


  • Salisu M Abdulrahman, P Brazdil, J van Rijn and J Vanschoren: Speeding up Algorithm Selection using Average Ranking and Active Testing by Introducing Runtime, accepted for the Special Issue on Metalearning and Algorithm Selection, Machine Learning Journal, to be published in 2017 


  • Catarina F Oliveira, C Soares, A Jorge, Can metalearning be applied to transfer on heterogeneous datasets?, In procedings of HAIS 2016 - 11th International Conference on Hybrid Artificial Intelligence Systems, Seville, Spain, Abril, 2016.
  • J Kanda, A de Carvalho, E Hruschka, C Soares, P Brazdil, Metalearning to select the best metaheuristic for the Traveling Salesman Problem: A comparison of metafeatures, Neurocomputing, 2016
  • S Abdulrahman, P Brazdil: Effect of Incomplete Meta-dataset on Average Ranking Method, presented at Auto@ML at ICML-2016. 


  • S Abdulrahman, P Brazdil, J van Rijn and J Vanschoren:  Algorithm Selection via Metalearning and Sample-based Active Testing,  Proc. of MetaSel-2015 workshop of ECML/PKDD-2015
  • J. van Rijn, S Abdulrahman, P Brazdil and J. Vanschoren: Fast Algorithm Selection using Learning Curves, Proc. of IDA-2015, Springer, 2015
  • F Pinto, C Soares, P Brazdil, Combining regression models and metaheuristics to optimize space allocation in the retail industry, Intelligent Data Analysis 19, 149-162, 2015
  • F Pinto, C Soares, J Mendes-Moreira: Pruning bagging ensembles with metalearning, International Workshop on Multiple Classifier Systems, 64-75


  • S Abdulrahman and P Brazdil Measures for Combining Accuracy and Time for Meta-learning, Proc. of MetaSel-2014 workshop of ECAI-2014.
  • Fábio Pinto, C Soares and J Mendes-Moreira: A Framework To Decompose And Develop Metafeatures, Proc. of MetaSel-2014 workshop of ECAI-2014
  • P Miranda, R Prudêncio, A. Carvalho, C Soares: A hybrid meta-learning architecture for multi-objective optimization of SVM parameters, Neurocomputing, Vol.143, 2014


  • R Leite, P Brazdil, Joaquin Vanschoren: Selecting Classification Algorithms with Active Testing. in Proc. of MLDM 2012: 117-131, Springer
  • T Gomes, R Prudêncio, C Soares, A Rossi, A Carvalho: Combining meta-learning and search techniques to select parameters for support vector machines. Neurocomputing 75(1): 3-13 (2012)
  • P Miranda, R Prudêncio, A Carvalho, C Soares: Combining a multi-objective optimization approach with meta-learning for SVM parameter selection, in 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, pp. 2909-2914, 2012
  • P Miranda, R Prudêncio, R Cavalcante, A Carvalho, C Soares: An Experimental Study of the Combination of Meta-Learning with Particle Swarm Algorithms for SVM Parameter Selection, in Computational Science and Its Applications, ICCSA 2012 - 12th International Conference, ed. B.Murgante et al., Salvador de Bahia, Brazil, Springer, 2012
  • P Miranda, R Prudêncio, A Carvalho, C Soares: Combining Meta-Learning with Multi-objective Particle Swarm Algorithms for SVM Parameter Selection: An Experimental Analysis, in 2012 Brazilian Symposium on Neural Networks, IEEE, 2012
  • J Kanda, C Soares, E Hruschka, A Carvalho: A Meta-Learning Approach to Select Meta-Heuristics for the Traveling Salesman Problem Using MLP-Based Label Ranking. ICONIP (3), 2012: 488-495
  • P Miranda, R Prudêncio, A Carvalho, C Soares: Multi-objective optimization and Meta-learning for SVM parameter selection. IJCNN 2012: 1-8


  • J Kanda, A Carvalho, E Hruschka, C Soares: Selection of algorithms to solve traveling salesman problems using meta-learning. Int. J. Hybrid Intell. Syst. 8(3): 117-128 (2011)
  • R Prudêncio, C Soares, T Ludermir: Combining Meta-learning and Active Selection of Datasetoids for Algorithm Selection. HAIS (1), 2011: 164-171
  • J Kanda, A Carvalho, E Hruschka, C Soares: Using Meta-learning to Recommend Meta-heuristics for the Traveling Salesman Problem. ICMLA (1) 2011: 346-351
  • R Leite, P Brazdil: Active Testing Strategy to Predict the Best Classification Algorithm via Sampling and Metalearning. ECAI 2010: 309-314
  • T Gomes, R Prudêncio, C Soares, A Rossi, A Carvalho: Combining Meta-learning and Search Techniques to SVM Parameter Selection. SBRN 2010: 79-84
  • P Brazdil, R Vilalta, Ch Giraud-Carrier, C Soares: Metalearning. Encyclopedia of Machine Learning 2010: 662-666


  • R Leite, P. Brazdil, Predicting Relative Performance of Classifiers from Samples, Proc. of the 22nd Int. Conf. on Machine Learning (ICML-2005), ACM, 2005


Ph.D. Theses Completed:

  • Carlos Soares: Learning Ranking s of Learning Algorithms, 2004
  • Rui Leite: Control of Process of Knowledge Extraction from Data and Data Mining, FCUP, Porto, 2008

PhD Theses in Progress

  • Fabio Pinto: Metalearning for Dynamic Integration in Ensemble Methods.
  • Salisu M Abdulrahman: Development of Support System for Workflow Design for Data Mining Problems that Exploit Meta-learning, MAPi.

Recent Events (Workshops):

Metalearning and Algorithm Selection Google Group