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Title: Estimating prediction certainty in decision trees
Authors: De Paula Costa, Eduardo
Verwer, Sicco
Blockeel, Hendrik
Issue Date: Oct-2013
Publisher: Springer
Host Document: Lecture Notes in Computer Science vol:8207 pages:138-149
Conference: Intelligent Data Analysis edition:12 location:London, UK date:17-19 October 2013
Abstract: Decision trees estimate prediction certainty using the class distribution in
the leaf responsible for the prediction. We introduce an alternative method
that yields better estimates. For each instance to be predicted, our method
inserts the instance to be classified in the training set with one of the
possible labels for the target attribute; this procedure is repeated for each
one of the labels. Then, by comparing the outcome of the different trees, the
method can identify instances that might present some difficulties to be
correctly classified, and attribute some uncertainty to their prediction. We
perform an extensive evaluation of the proposed method, and show that it is
particularly suitable for ranking and reliability estimations. The ideas
investigated in this paper may also be applied to other machine learning
techniques, as well as combined with other methods for prediction certainty
estimation.
URI: 
ISBN: 978-3-642-41398-8
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section

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