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19th IEEE International Conference on Tools with Artificial Intelligence - Vol.2 (ICTAI 2007)
An Empirical Study of Learning from Imbalanced Data Using Random Forest
Paris, France
October 29-October 31
ISBN: 0-7695-3015-X
ASCII Text
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Taghi M. Khoshgoftaar, Moiz Golawala, Jason Van Hulse, "An Empirical Study of Learning from Imbalanced Data Using Random Forest," 2012 IEEE 24th International Conference on Tools with Artificial Intelligence, vol. 2, pp. 310-317, 19th IEEE International Conference on Tools with Artificial Intelligence - Vol.2 (ICTAI 2007), 2007.
 
 
BibTex
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@article{ 10.1109/ICTAI.2007.46,
author = {Taghi M. Khoshgoftaar and Moiz Golawala and Jason Van Hulse},
title = {An Empirical Study of Learning from Imbalanced Data Using Random Forest},
journal ={2012 IEEE 24th International Conference on Tools with Artificial Intelligence},
volume = {2},
year = {2007},
issn = {1082-3409},
pages = {310-317},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICTAI.2007.46},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - 2012 IEEE 24th International Conference on Tools with Artificial Intelligence
TI - An Empirical Study of Learning from Imbalanced Data Using Random Forest
SN - 1082-3409
SP310
EP317
A1 - Taghi M. Khoshgoftaar,
A1 - Moiz Golawala,
A1 - Jason Van Hulse,
PY - 2007
VL - 2
JA - 2012 IEEE 24th International Conference on Tools with Artificial Intelligence
ER -
 
This paper discusses a comprehensive suite of experi- ments that analyze the performance of the random forest (RF) learner implemented in Weka. RF is a relatively new learner, and to the best of our knowledge, only preliminary experimentation on the construction of random forest clas- sifiers in the context of imbalanced data has been reported in previous work. Therefore, the contribution of this study is to provide an extensive empirical evaluation of RF learn- ers built from imbalanced data. What should be the rec- ommended default number of trees in the ensemble? What should the recommended value be for the number of at- tributes? How does the RF learner perform on imbalanced data when compared with other commonly-used learners? We address these and other related issues in this work.
Citation:
Taghi M. Khoshgoftaar, Moiz Golawala, Jason Van Hulse, "An Empirical Study of Learning from Imbalanced Data Using Random Forest," ictai, vol. 2, pp.310-317, 19th IEEE International Conference on Tools with Artificial Intelligence - Vol.2 (ICTAI 2007), 2007
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