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Classification Using Streaming Random Forests
January 2011 (vol. 23 no. 1)
pp. 22-36
ASCII Text
x 
 
Hanady Abdulsalam, David B. Skillicorn, Patrick Martin, "Classification Using Streaming Random Forests," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 1, pp. 22-36, January, 2011.
 
 
BibTex
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@article{ 10.1109/TKDE.2010.36,
author = {Hanady Abdulsalam and David B. Skillicorn and Patrick Martin},
title = {Classification Using Streaming Random Forests},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {23},
number = {1},
issn = {1041-4347},
year = {2011},
pages = {22-36},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.36},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Classification Using Streaming Random Forests
IS - 1
SN - 1041-4347
SP22
EP36
EPD - 22-36
A1 - Hanady Abdulsalam,
A1 - David B. Skillicorn,
A1 - Patrick Martin,
PY - 2011
KW - Data stream mining
KW - data stream classification
KW - decision tree ensembles
KW - random forests.
VL - 23
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Hanady Abdulsalam, Kuwait University, Kuwait
David B. Skillicorn, Queen's University, Kingston
Patrick Martin, Queen's University, Kingston
We consider the problem of data stream classification, where the data arrive in a conceptually infinite stream, and the opportunity to examine each record is brief. We introduce a stream classification algorithm that is online, running in amortized {\cal O}(1) time, able to handle intermittent arrival of labeled records, and able to adjust its parameters to respond to changing class boundaries (“concept drift”) in the data stream. In addition, when blocks of labeled data are short, the algorithm is able to judge internally whether the quality of models updated from them is good enough for deployment on unlabeled records, or whether further labeled records are required. Unlike most proposed stream-classification algorithms, multiple target classes can be handled. Experimental results on real and synthetic data show that accuracy is comparable to a conventional classification algorithm that sees all of the data at once and is able to make multiple passes over it.

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Index Terms:
Data stream mining, data stream classification, decision tree ensembles, random forests.
Citation:
Hanady Abdulsalam, David B. Skillicorn, Patrick Martin, "Classification Using Streaming Random Forests," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 1, pp. 22-36, Jan. 2011, doi:10.1109/TKDE.2010.36
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