Title: Ensemble-trees: Leveraging ensemble power inside decision trees
Authors: Zimmermann, Albrecht # ×
Issue Date: Oct-2008
Publisher: Springer
Series Title: Lecture Notes in Computer Science vol:5255 pages:76-87
Conference: Discovery Science edition:11 location:Budapest date:13-16 October 2008
Abstract: Decision trees are among the most effective and interpretable classification algorithms while ensembles techniques have been proven to alleviate problems regarding over-fitting and variance. On the other hand, decision trees show a tendency to lack stability given small changes in the data, whereas interpreting an ensemble of trees is challenging to comprehend. We propose the technique of Ensemble-Trees which uses ensembles of rules within the test nodes to reduce over-fitting and variance effects. Validating the technique experimentally, we find that improvements in performance compared to ensembles of pruned trees exist, but also that the technique does less to reduce structural instability than could be expected.
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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