Meta-Learning Based Framework for Helping Non-expert Miners to Choice a Suitable Classification Algorithm: An Application for the Educational Field

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9330)


One of the most challenging tasks in the knowledge discovery process is the selection of the best classification algorithm for a data set at hand. Thus, tools which help practitioners to choose the best classifier along with its parameter setting are highly demanded. These will not only be useful for trainees but also for the automation of the data mining process. Our approach is based on meta-learning, which relies on the application of learning algorithms on meta-data extracted from data mining experiments in order to better understand how these algorithms can become flexible in solving different kinds of learning problems. This paper presents a framework which allows novices to create and feed their own experiment database and later, analyse and select the best technique for their target data set. As case study, we evaluate different sets of meta-features on educational data sets and discuss which ones are more suitable for predicting student performance.


Meta-learning Regression Student performance 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cavalcanti, G., Ren, T., Vale, B.: Data complexity measures and nearest neighbor classifiers: a practical analysis for meta-learning. In: 2012 IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI), vol. 1, pp. 1065–1069, November 2012Google Scholar
  2. 2.
    Romero, C., Olmo, J.L., Ventura, S.: A meta-learning approach for recommending a subset of white-box classification algorithms for Moodle datasets. In: Proc. 6th Int. Conference on Educational Data Mining, pp. 268–271 (2013)Google Scholar
  3. 3.
    Espinosa, R., García-Saiz, D., Zorrilla, M.E., Zubcoff, J.J., Mazón, J.: Development of a knowledge base for enabling non-expert users to apply data mining algorithms. In: Accorsi, R., Ceravolo, P., Cudré-Mauroux, P. (eds.) Proceedings of the 3rd International Symposium on Data-driven Process Discovery and Analysis, Riva del Garda, Italy, August 30, 2013. CEUR Workshop Proceedings, vol. 1027, pp. 46–61. (2013).
  4. 4.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: The kdd process for extracting useful knowledge from volumes of data. Commun. ACM 39(11), 27–34 (1996)CrossRefGoogle Scholar
  5. 5.
    Hilario, M., Kalousis, A.: Building algorithm profiles for prior model selection in knowledge discovery systems. Engineering Intelligent Systems 8, 956–961 (2002)Google Scholar
  6. 6.
    Ho, T.K.: Geometrical complexity of classification problems (2004). CoRR cs.CV/0402020Google Scholar
  7. 7.
    Kalousis, A., Hilario, M.: Model selection via meta-learning: a comparative study. In: Proc. 12th IEEE International Conference on Tools with Artificial Intelligence, pp. 406–413 (2000)Google Scholar
  8. 8.
    Köpf, C., Taylor, C., Keller, J.: Meta-analysis: from data characterisation for meta-learning to meta-regression. In: Proceedings of the PKDD-00 Workshop on Data Mining, Decision Support, Meta-Learning and ILP (2000)Google Scholar
  9. 9.
    Kordík, P., Cerný, J.: On performance of meta-learning templates on different datasets. In: IJCNN, pp. 1–7. IEEE (2012)Google Scholar
  10. 10.
    Molina, M.M., Luna, J.M., Romero, C., Ventura, S.: Meta-learning approach for automatic parameter tuning: a case study with educational datasets. In: Proc. 5th International Conference on Educational Data Mining, pp. 180–183 (2012)Google Scholar
  11. 11.
    Peng, Y.H., Flach, P.A., Soares, C., Brazdil, P.B.: Improved dataset characterisation for meta-learning. In: Lange, S., Satoh, K., Smith, C.H. (eds.) DS 2002. LNCS, vol. 2534, pp. 141–152. Springer, Heidelberg (2002) CrossRefGoogle Scholar
  12. 12.
    Pfahringer, B., Bensusan, H., Giraud-carrier, C.: Meta-learning by landmarking various learning algorithms. In: Proceedings of the 17th International Conference on Machine Learning, pp. 743–750. Morgan Kaufmann (2000)Google Scholar
  13. 13.
    Reif, M., Leveringhaus, A., Shafait, F., Dengel, A.: Predicting classifier combinations. In: Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods. INSTICC, SciTePress (2013)Google Scholar
  14. 14.
    Reif, M., Shafait, F., Goldstein, M., Breuel, T., Dengel, A.: Automatic classifier selection for non-experts. Pattern Analysis and Applications 17(1), 83–96 (2014). MathSciNetCrossRefGoogle Scholar
  15. 15.
    Rice, J.: The algorithm selection problem. Adv. Comput. 15, 65–118 (1976)CrossRefGoogle Scholar
  16. 16.
    Romero, C., Ventura, S.: Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3(1), 12–27 (2013)Google Scholar
  17. 17.
    Segrera, S., Pinho, J., Moreno, M.N.: Information-theoretic measures for meta-learning. In: Corchado, E., Abraham, A., Pedrycz, W. (eds.) HAIS 2008. LNCS (LNAI), vol. 5271, pp. 458–465. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  18. 18.
    Vanschoren, J., Blockeel, H., Pfahringer, B., Holmes, G.: Experiment databases. Machine Learning 87(2), 127–158 (2012). MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Vilalta, R., Drissi, Y.: A perspective view and survey of meta-learning. Artificial Intelligence Review 18, 77–95 (2002)CrossRefGoogle Scholar
  20. 20.
    Wolpert, D.H.: The supervised learning no-free-lunch theorems. In: Proc. 6th Online World Conference on Soft Computing in Industrial Applications, pp. 25–42 (2001)Google Scholar
  21. 21.
    Zorrilla, M., García-Saiz, D.: Meta-learning: can it be suitable to automatise the KDD process for the educational domain? In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds.) RSEISP 2014. LNCS, vol. 8537, pp. 285–292. Springer, Heidelberg (2014). Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer Science and ElectronicsUniversity of CantabriaSantanderSpain

Personalised recommendations