Advertisement

Ranking with Predictive Clustering Trees

  • Ljupco Todorovski
  • Hendrik Blockeel
  • Saso Dzeroski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2430)

Abstract

A novel class of applications of predictive clustering trees is addressed, namely ranking. Predictive clustering trees, as implemented in Clus, allow for predicting multiple target variables. This approach makes sense especially if the target variables are not independent of each other. This is typically the case in ranking, where the (relative) performance of several approaches on the same task has to be predicted from a given description of the task. We propose to use predictive clustering trees for ranking. As compared to existing ranking approaches which are instance-based, our approach also allows for an explanation of the predicted rankings. We illustrate our approach on the task of ranking machine learning algorithms, where the (relative) performance of the learning algorithms on a dataset has to be predicted from a given dataset description.

Keywords

Regression Tree Individual Attribute Machine Learning Algorithm Cluster Tree Ranking Tree 
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.

References

  1. [1]
    ESPRIT METAL Project (project number 26.357): A Meta-Learning Assistant for Providing User Support in Machine Learning and Data Mining. http://www.metal-kdd.org/.
  2. [2]
    H. Bensusan and A. Kalousis. Estimating the predictive accuracy of a classifier. In Proc. of the Twelfth European Conference on Machine Learning, pages 25–36. Springer, Berlin, 2001.Google Scholar
  3. [3]
    C. L. Blake and C. J. Merz. UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html. Irvine, CA: University of California, Department of Information and Computer Science, 1998.Google Scholar
  4. [4]
    H. Blockeel, L. De Raedt, and J. Ramon. Top-down induction of clustering trees. In Proc. of the Fifteenth International Conference on Machine Learning, pages 55–63. Morgan Kaufmann, 1998.Google Scholar
  5. [5]
    H. Blockeel, L. Dehaspe, B. Demoen, G. Janssens, J. Ramon, and H. Vandecasteele. Improving the efficiency of inductive logic programming through the use of query packs. Journal of Artificial Intelligence Research, 2002. In press.Google Scholar
  6. [6]
    P. B. Brazdil and R. J. Henery. Analysis of results. In D. Michie, D. J. Spiegelhalter, and C. C. Taylor, editors, Machine learning, neural and statistical classification, pages 98–106. Ellis Horwood, Chichester, 1994.Google Scholar
  7. [7]
    A. Kalousis. Algorithm Selection via Meta-Learning. PhD Thesis. University of Geneva, Department of Computer Science, 2002.Google Scholar
  8. [8]
    A. Kalousis and T. Theoharis. NEOMON: design, implementation and performance results of an intelligent assistant for classifier selection. Intelligent Data Analysis 3(5): 319–337, 1999.MATHCrossRefGoogle Scholar
  9. [9]
    G. Lindner and R. Studer. AST: Support for algorithm selection with a CBR approach. In Proc. of the ICML-99 Workshop on Recent Advances in Meta-Learning and Future Work, pages 38–47. J. Stefan Institute, Ljubljana, Slovenia, 1999.Google Scholar
  10. [10]
    B. Pfahringer, H. Bensusan and C. Giraud-Carrier. Meta-Learning by Landmarking Various Learning Algorithms. In Proc. of the Seventeenth International Conference on Machine Learning: 743–750. Morgan Kaufmann, San Francisco, 2000.Google Scholar
  11. [11]
    C. Soares and P. B. Brazdil. Zoomed ranking: Selection of classi.cation algorithms based on relevant performance information. In Proc. of the Fourth European Conference on Principles of Data Mining and Knowledge Discovery, pages 126–135. Springer, Berlin, 2000.Google Scholar
  12. [12]
    L. Todorovski and S. Dzeroski. Experiments in meta-level learning with ILP. In Proc. of the Third European Conference on Principles of Data Mining and Knowledge Discovery, pages 98–106. Springer, Berlin, 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Ljupco Todorovski
    • 1
  • Hendrik Blockeel
    • 2
  • Saso Dzeroski
    • 1
  1. 1.Department of Intelligent SystemsJozef Stefan InstituteLjubljanaSlovenia
  2. 2.Department of Computer ScienceKatholieke Universiteit LeuvenHeverleeBelgium

Personalised recommendations