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Third IEEE International Conference on Data Mining (ICDM'03)
Comparing Pure Parallel Ensemble Creation Techniques Against Bagging
Melbourne, Florida
November 19-November 22
ISBN: 0-7695-1978-4
ASCII Text
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Lawrence O. Hall, Kevin W. Bowyer, Robert E. Banfield, Divya Bhadoria, W. Philip Kegelmeyer, Steven Eschrich, "Comparing Pure Parallel Ensemble Creation Techniques Against Bagging," Data Mining, IEEE International Conference on, pp. 533, Third IEEE International Conference on Data Mining (ICDM'03), 2003.
 
 
BibTex
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@article{ 10.1109/ICDM.2003.1250970,
author = {Lawrence O. Hall and Kevin W. Bowyer and Robert E. Banfield and Divya Bhadoria and W. Philip Kegelmeyer and Steven Eschrich},
title = {Comparing Pure Parallel Ensemble Creation Techniques Against Bagging},
journal ={Data Mining, IEEE International Conference on},
volume = {0},
year = {2003},
isbn = {0-7695-1978-4},
pages = {533},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250970},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Data Mining, IEEE International Conference on
TI - Comparing Pure Parallel Ensemble Creation Techniques Against Bagging
SN - 0-7695-1978-4
SP
EP
A1 - Lawrence O. Hall,
A1 - Kevin W. Bowyer,
A1 - Robert E. Banfield,
A1 - Divya Bhadoria,
A1 - W. Philip Kegelmeyer,
A1 - Steven Eschrich,
PY - 2003
KW - null
VL - 0
JA - Data Mining, IEEE International Conference on
ER -
 
Lawrence O. Hall, University of South Florida, Tampa
Kevin W. Bowyer, Computer Science & Engineering, Notre Dame, IN
Robert E. Banfield, University of South Florida, Tampa
Divya Bhadoria, University of South Florida, Tampa
W. Philip Kegelmeyer, Sandia National Labs, Livermore, CA
Steven Eschrich, University of South Florida, Tampa
We experimentally evaluate randomization-based approaches to creating an ensemble of decision-tree classifiers. Unlike methods related to boosting, all of the eight approaches considered here create each classifier in an ensemble independently of the other classifiers. Experiments were performed on 28 publicly available datasets, using C4.5 release 8 as the base classifier. While each of the other seven approaches has some strengths, we find that none of them is consistently more accurate than standard bagging when tested for statistical significance.
Citation:
Lawrence O. Hall, Kevin W. Bowyer, Robert E. Banfield, Divya Bhadoria, W. Philip Kegelmeyer, Steven Eschrich, "Comparing Pure Parallel Ensemble Creation Techniques Against Bagging," icdm, pp.533, Third IEEE International Conference on Data Mining (ICDM'03), 2003
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