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)


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.


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.


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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

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