Actions for Creating Ensembles of Decision Trees Through Sampling [electronic resource].

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Creating Ensembles of Decision Trees Through Sampling [electronic resource].

Published
Washington, D.C. : United States. Dept. of Energy, 2001.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Physical Description
PDF-FILE: 11 ; SIZE: 0.2 MBYTES pages
Additional Creators
Lawrence Livermore National Laboratory, United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
Recent work in classification indicates that significant improvements in accuracy can be obtained by growing an ensemble of classifiers and having them vote for the most popular class. This paper focuses on ensembles of decision trees that are created with a randomized procedure based on sampling. Randomization can be introduced by using random samples of the training data (as in bagging or boosting) and running a conventional tree-building algorithm, or by randomizing the induction algorithm itself. The objective of this paper is to describe the first experiences with a novel randomized tree induction method that uses a sub-sample of instances at a node to determine the split. The empirical results show that ensembles generated using this approach yield results that are competitive in accuracy and superior in computational cost to boosting and bagging.
Report Numbers
E 1.99:ucrl-jc-142268-rev-1
ucrl-jc-142268-rev-1
Subject(s)
Other Subject(s)
Note
Published through SciTech Connect.
07/26/2001.
"ucrl-jc-142268-rev-1"
33rd Symposium on the Interface: Computing Science and Statistics, Costa Mesa, CA (US), 06/13/2001--06/16/2001.
Kamath,C; Cantu-Paz, E.
Funding Information
W-7405-ENG-48
View MARC record | catkey: 14348078