Advertisement

Novel Interestingness Measures for Mining Significant Association Rules from Imbalanced Data

  • Safa AbdellatifEmail author
  • Mohamed Ali Ben Hassine
  • Sadok Ben Yahia
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)

Abstract

Associative classification is a rule-based approach that joins Association Rule Mining and Classification to build classifiers that predict class labels for new data. Associative classifiers may generate an overwhelming number of rules which are hard to handle. Delving through these rules to identify the most interesting ones is a challenging task. To overcome this problem, several measures have been proposed. However, for imbalanced datasets, existing measures are no more reliable. In fact, they tend either to favour rules of major classes and consider others as uninteresting or only emphasize on the rules of minor classes and omit other ones. In this respect, we propose five new measures which tend to be fair for both types of classes regardless of their imbalanced distribution. Extensive carried out experiments on real-world datasets show that the new measures are able to efficiently extract significant knowledge from minor classes without decreasing the global predictive accuracy.

Keywords

Association rules Interestingness measures Imbalanced datasets 

References

  1. 1.
    Abdellatif, S., Ben Hassine, M.A., Ben Yahia, S., Bouzeghoub, A.: ARCID: a new approach to deal with imbalanced datasets classification. In: International Conference on Current Trends in Theory and Practice of Informatics. Springer (2018)Google Scholar
  2. 2.
    Abdellatif, S., Ben Yahia, S., Ben Hassine, M.A., Bouzeghoub, A.: Fuzzy aggregation for rule selection in imbalanced datasets classification using choquet integral. In: 2018 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2018, Rio de Janeiro, Brazil, 8–13 July 2018 (2018)Google Scholar
  3. 3.
    Cohen, W.W.: Fast effective rule induction. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 115–123 (1995)Google Scholar
  4. 4.
    Hu, B.G., Dong, W.M.: A study on cost behaviors of binary classification measures in class-imbalanced problems. arXiv preprint arXiv:1403.7100 (2014)
  5. 5.
    Lenca, P., Vaillant, B., Meyer, P., Lallich, S.: Association rule interestingness measures: experimental and theoretical studies. In: Quality Measures in Data Mining, pp. 51–76. Springer (2007)Google Scholar
  6. 6.
    Ma, Y., Hsu, W., Liu, B.: Integrating classification and association rule mining. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (1998)Google Scholar
  7. 7.
    Major, J.A., Mangano, J.J.: Selecting among rules induced from a hurricane database. J. Intell. Inf. Syst. 4(1), 39–52 (1995)CrossRefGoogle Scholar
  8. 8.
    Merz, C.: UCI repository of machine learning databases (1996). http://www.ics.uci.edu/~mlearn/MLRepository.html
  9. 9.
    Piatetsky-Shapiro, G.: Discovery, analysis, and presentation of strong rules. In: Knowledge Discovery in Databases, pp. 229–238 (1991)Google Scholar
  10. 10.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Safa Abdellatif
    • 1
    Email author
  • Mohamed Ali Ben Hassine
    • 1
  • Sadok Ben Yahia
    • 1
    • 2
  1. 1.Faculty of Sciences of TunisUniversity of Tunis El ManarTunisTunisia
  2. 2.Department of Software ScienceTallinn University of TechnologyTallinnEstonia

Personalised recommendations