A Parameter-Free Associative Classification Method

  • Loïc Cerf
  • Dominique Gay
  • Nazha Selmaoui
  • Jean-François Boulicaut
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5182)

Abstract

In many application domains, classification tasks have to tackle multiclass imbalanced training sets. We have been looking for a CBA approach (Classification Based on Association rules) in such difficult contexts. Actually, most of the CBA-like methods are one-vs-all approaches (OVA), i.e., selected rules characterize a class with what is relevant for this class and irrelevant for the union of the other classes. Instead, our method considers that a rule has to be relevant for one class and irrelevant for every other class taken separately. Furthermore, a constrained hill climbing strategy spares users tuning parameters and/or spending time in tedious post-processing phases. Our approach is empirically validated on various benchmark data sets.

Keywords

Classification Association Rules Parameter Tuning Multiclass 

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References

  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining Association Rules Between Sets of Items in Large Databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216. ACM Press, New York (1993)CrossRefGoogle Scholar
  2. 2.
    Liu, B., Hsu, W., Ma, Y.: Integrating Classification and Association Rule Mining. In: Proceedings of the Fourth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 80–86. AAAI Press, Menlo Park (1998)Google Scholar
  3. 3.
    Bayardo, R., Agrawal, R.: Mining the Most Interesting Rules. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 145–154. ACM Press, New York (1999)CrossRefGoogle Scholar
  4. 4.
    Li, W., Han, J., Pei, J.: CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules. In: Proceedings of the First IEEE International Conference on Data Mining, pp. 369–376. IEEE Computer Society, Los Alamitos (2001)Google Scholar
  5. 5.
    Boulicaut, J.F., Crémilleux, B.: Simplest Rules Characterizing Classes Generated by Delta-free Sets. In: Proceedings of the Twenty-Second Annual International Conference Knowledge Based Systems and Applied Artificial Intelligence, pp. 33–46. Springer, Heidelberg (2002)Google Scholar
  6. 6.
    Yin, X., Han, J.: CPAR: Classification Based on Predictive Association Rules. In: Proceedings of the Third SIAM International Conference on Data Mining, pp. 369–376. SIAM, Philadelphia (2003)Google Scholar
  7. 7.
    Baralis, E., Chiusano, S.: Essential Classification Rule Sets. ACM Transactions on Database Systems 29(4), 635–674 (2004)CrossRefGoogle Scholar
  8. 8.
    Bouzouita, I., Elloumi, S., Yahia, S.B.: GARC: A New Associative Classification Approach. In: Proceedings of the Eight International Conference on Data Ware-housing and Knowledge Discovery, pp. 554–565. Springer, Heidelberg (2006)Google Scholar
  9. 9.
    Freitas, A.A.: Understanding the Crucial Differences Between Classification and Discovery of Association Rules – A Position Paper. SIGKDD Explorations 2(1), 65–69 (2000)CrossRefGoogle Scholar
  10. 10.
    Wang, J., Karypis, G.: HARMONY: Efficiently Mining the Best Rules for Classification. In: Proceedings of the Fifth SIAM International Conference on Data Mining, pp. 34–43. SIAM, Philadelphia (2005)Google Scholar
  11. 11.
    Arunasalam, B., Chawla, S.: CCCS: A Top-down Associative Classifier for Imbalanced Class Distribution. In: Proceedings of the Twelveth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 517–522. ACM Press, New York (2006)Google Scholar
  12. 12.
    Verhein, F., Chawla, S.: Using Significant Positively Associated and Relatively Class Correlated Rules for Associative Classification of Imbalanced Datasets. In: Proceedings of the Seventh IEEE International Conference on Data Mining, pp. 679–684. IEEE Computer Society Press, Los AlamitosGoogle Scholar
  13. 13.
    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, pp. 43–52. ACM Press, New York (1999)CrossRefGoogle Scholar
  14. 14.
    Ramamohanarao, K., Fan, H.: Patterns Based Classifiers. World Wide Web 10(1), 71–83 (2007)CrossRefGoogle Scholar
  15. 15.
    Cerf, L., Gay, D., Selmaoui, N., Boulicaut, J.F.: Technical Notes on fitcare’s Implementation. Technical report, LIRIS (April 2008)Google Scholar
  16. 16.
    Coenen, F.: The LUCS-KDD software library (2004), http://www.csc.liv.ac.uk/~frans/KDD/Software/.
  17. 17.
    Newman, D., Hettich, S., Blake, C., Merz, C.: UCI Repository of Machine Learning Databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Loïc Cerf
    • 1
  • Dominique Gay
    • 2
  • Nazha Selmaoui
    • 2
  • Jean-François Boulicaut
    • 1
  1. 1.INSA-Lyon, LIRIS CNRS UMR5205, F-69621 VilleurbanneFrance
  2. 2.Université de la Nouvelle-Calédonie, ERIM EA 3791, 98800 Nouméa, Nouvelle-Calédonie 

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