Exploring Query Matrix for Support Pattern Based Classification Learning

  • Yiqiu Han
  • Wai Lam
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)


This paper explores the customized learning from specific to general for classification learning. Our novel learning framework called SUPE customizes its learning process to the instance to be classified called query instance. The data representation in SUPE is also customized to the query instance. Given a query instance, the training data is transformed into a query matrix, from which useful patterns are discovered for learning. The final prediction of the class label is performed by combining some statistics of the discovered useful patterns. We show that SUPE conducts the search from specific to general in a significantly reduced hypothesis space. The query matrix also facilitates the complicated operations in SUPE. The experimental results on benchmark data sets are encouraging.


Class Label Training Instance Hypothesis Space Support Pattern Query Instance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Blake, C., Keogh, E., Merz, C.: UCI repository of machine learning databases,
  2. 2.
    Cohen, W.: Fast effective rule induction. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 115–123 (1995)Google Scholar
  3. 3.
    Cohen, W., Singer, Y.: A simple, fast, and effective rule learner. In: Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence, pp. 335–342 (1999)Google Scholar
  4. 4.
    Han, Y., Lam, W.: Lazy learning by scanning memory image lattice. In: Proceedings of the 2004 SIAM International Conference on Data Mining, pp. 407–423 (2004)Google Scholar
  5. 5.
    Han, Y., Lam, W.: Lazy learning for classification based on query projections. In: Proceedings of the 2005 SIAM International Conference on Data Mining, pp. 227–238 (2005)Google Scholar
  6. 6.
    Lam, W., Han, Y.: Automatice textual document categorization based on generalized instance sets and a metamodel. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5), 628–633 (2003)CrossRefGoogle Scholar
  7. 7.
    Li, W., Han, J., Pei, J.: Accurate and efficient classification based on multiple class-association rules. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), pp. 369–376 (2001)Google Scholar
  8. 8.
    Mehta, M., Rissanen, J., Agrawal, R.: MDL-based decision tree pruning. In: Proceedings of the First International Conference on Knowledge Discovery and Data Mining, KDD 1995, pp. 216–221 (1995)Google Scholar
  9. 9.
    Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)MATHGoogle Scholar
  10. 10.
    Weiss, S., Indurkhya, N.: Lightweight rule induction. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 1135–1142. Morgan Kaufmann, San Francisco (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yiqiu Han
    • 1
  • Wai Lam
    • 1
  1. 1.Department of Systems Engineering and Engineering ManagementThe Chinese University of Hong KongShatin, Hong Kong

Personalised recommendations