Title: Empirical asymmetric selective transfer in multi-objective decision trees
Authors: Piccart, Beau ×
Struyf, Jan
Blockeel, Hendrik #
Issue Date: 13-Oct-2008
Publisher: Springer
Series Title: Lecture Notes in Computer Science vol:5255 pages:64-75
Conference: International conference on Discovery Science edition:11 location:Budapest date:13-16 October 2008
Abstract: We consider learning tasks where multiple target variables
need to be predicted. Two approaches have been used in this setting:
(a) build a separate single-target model for each target variable, and (b)
build a multi-target model that predicts all targets simultaneously; the
latter may exploit potential dependencies among the targets. For a given
target, either (a) or (b) can yield the most accurate model. This shows
that exploiting information available in other targets may be beneficial
as well as detrimental to accuracy. This raises the question whether it is
possible to find, for a given target (we call this the main target), the best
subset of the other targets (the support targets) that, when combined
with the main target in a multi-target model, results in the most accurate
model for the main target. We propose Empirical Asymmetric Selective
Transfer (EAST), a generally applicable algorithm that approximates
such a subset. Applied to decision trees, EAST outperforms single-target
decision trees, multi-target decision trees, and multi-target decision trees
with target clustering.
ISBN: 978-3-540-88410-1
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
SIT-PiccartEtAl-DS08.pdf Published 198KbAdobe PDFView/Open


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science