Evaluating Restrictions in Pattern Based Classifiers

  • Andy González-Méndez
  • Diana Martín-Rodríguez
  • Milton García-BorrotoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11896)


Generalizations, also known as patterns, are in the core of many learning systems. A key component for automatically mine generalizations is to define the predicate to select the most important ones. This predicate is usually expressed as a conjunction of restrictions. In this paper, we present an experimental study of some of the most used restrictions: the minimal support threshold, the jumping pattern and the minimal pattern. This study that uses 93 databases and two different classifiers reveals some interesting results, including one that should be very useful for building better classifiers: using minimal patterns could degrade the accuracy.


Pattern based classifiers Restrictions Experimental evaluation 


  1. 1.
    Bache, K., Lichman, M.: UCI Machine Learning Repository (2013).
  2. 2.
    Bay, S.D., Pazzani, M.J.: Detecting change in categorical data: mining contrast sets. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 1999, pp. 302–306. ACM, New York (1999)Google Scholar
  3. 3.
    Dong, G., Bailey, J.: Contrast Data Mining. Concepts, Algorithms, and Applications. Taylor & Francis, Abingdon (2013)Google Scholar
  4. 4.
    Dong, G., Li, J.: Efficient mining of emerging patterns: discovering trends and differences. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 1999, pp. 43–52. ACM, New York (1999)Google Scholar
  5. 5.
    Dong, G., Zhang, X., Wong, L., Li, J.: CAEP: classification by aggregating emerging patterns. In: Arikawa, S., Furukawa, K. (eds.) DS 1999. LNCS (LNAI), vol. 1721, pp. 30–42. Springer, Heidelberg (1999). Scholar
  6. 6.
    Fan, H., Ramamohanarao, K.: A Bayesian approach to use emerging patterns for classification (2003)Google Scholar
  7. 7.
    García-Borroto, M.: A restriction-based approach to generalizations. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds.) IWAIPR 2018. LNCS, vol. 11047, pp. 239–246. Springer, Cham (2018). Scholar
  8. 8.
    García-Vico, A., Carmona, C., Martín, D., García-Borroto, M., del Jesus, M.: An overview of emerging pattern mining in supervised descriptive rule discovery: taxonomy, empirical study, trends, and prospects. Wiley Interdiscip. Rev.: Data Mining Knowl. Discov. (2017). Scholar
  9. 9.
    Liu, X., Wu, J., Gu, F., Wang, J., He, Z.: Discriminative pattern mining and its applications in bioinformatics. Brief. Bioinform. 16(16), 884–900 (2015)CrossRefGoogle Scholar
  10. 10.
    Mitchell, T.M.: Generalization as search. Artif. Intell. 18(1982), 203–226 (1982)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Novak, P.K., Lavrač, N., Webb, G.I.: Supervised descriptive rule discovery: a unifying survey of contrast set, emerging pattern and subgroup mining. J. Mach. Learn. Res. 10, 377–403 (2009)zbMATHGoogle Scholar
  12. 12.
    Wrobel, S.: An algorithm for multi-relational discovery of subgroups. In: Komorowski, J., Zytkow, J. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 78–87. Springer, Heidelberg (1997). Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Universidad Tecnológica de la Habana José Antonio Echeverría, CUJAEMarianaoCuba

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