Title: Ranking with predictive clustering trees
Authors: Todorovski, Ljupco ×
Blockeel, Hendrik
Dzeroski, Saso #
Issue Date: 2002
Publisher: Springer
Host Document: Lecture notes in computer science vol:2430 pages:444-455
Conference: 13th European Conference on Machine Learning and 6th European Conference on Principles and Practice of Knowledge Discovery in Databases location:Helsinki, Finland date:August 19-23, 2002
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.
ISBN: 3-540-44036-4
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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