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Algorithm Selection via Metalearning and Planning


PhD's involved: Pavel Brazdil (FEP, coord., LIAAD), João Gama (FEP, LIAAD), Alípio Jorge
Collaboration: Carlos Soares (FEUP, CESE), Rui Leite (FEP),  Salisu Abdulrahman (U.Kano, Nigeria)
PhD students: Fábio Pinto (CESE).

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, >370 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, >360 cit.
  • J Gama, P Brazdil, Characterization of classification algorithms, Progress in Artificial Intelligence, 189-200, 1995, >160 cit.
  • P Brazdil, J Gama, B Henery, Characterizing the applicability of classification algorithms using meta-level learning, Machine Learning: ECML-94, 83-102, 1994, >160 cit.
  • C Giraud-Carrier, R Vilalta, P Brazdil, Introduction to the special issue on meta-learning, Machine learning 54 (3), 187-193, 2004, 160 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, >140 cit.
  • PB Brazdil, C Soares, A comparison of ranking methods for classification algorithm selection, Machine Learning: ECML 2000, 63-75, 2000, >140 cit
  • Y Peng, P Flach, C Soares, P Brazdil: Improved dataset characterisation for meta-learning, Discovery Science, Springer, Vol. 2534, 141-152, 2002, <110 cit.

  • C Soares, PB Brazdil: Zoomed Ranking: Selection of Classification Algorithms Based on Relevant Performance Information, LNCS 1910, 126-135, 2000, >100 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, in Special Issue on Metalearning and Algorithm Selection, Machine learning 107 (1), 2018 
  • P Brazdil, C Giraud-Carrier: Metalearning and Algorithm Selection: Progress, State of the Art and Introduction to the 2017 Special Issue, in Special Issue on Metalearning and Algorithm Selection, Machine learning 107 (1), 1-14, 2018


  • MV Cachada, SM Abdulrahman, and P Brazdil, Combining Feature and Algorithm Hyperparameter Selection using some Metalearning Methods, Proc. of Workshop AutoML 2017 assoc. with ECML/PKDD 2017, Macedonia
  • SM Abdulrahman, MV Cachada, and P Brazdil, Impact of Feature Selection and Algorithm Parametrization on Average Ranking Method via Metalearning, in European Congress on Computational Methods in Applied Sciences and Engineering, Springer, 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:

  • Salisu M AbdulrahmanDevelopment of Support System for Workflow Design for Data Mining Problems that Exploit Meta-learning, MAPi.
  • Rui Leite: Control of Process of Knowledge Extraction from Data and Data Mining, FCUP, Porto, 2008
  • Carlos SoaresLearning Ranking s of Learning Algorithms, 2004

PhD Theses in Progress

  • Fabio Pinto: Metalearning for Dynamic Integration in Ensemble Methods.

M.Sc Theses Completed:

  • Maria João Ferreira, Automated workflow design for classifying documents, MADSAD/FEP, 2017
  • Miguel Cachada, Automatic Design of Machine Learning Workflows, MADSAD/FEP, 2017

Recent Events (Workshops):

Metalearning and Algorithm Selection Google Group