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DRFLogitBoost: A Double Randomized Decision Forest
Incorporated with LogitBoosted Decision Stumps
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Zaman Md. Faisal, Sumi, S.M and Hideo Hirose
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| 4th
Asian Conference on Intelligent Information and Database Systems
(ACIIDS 2012), March 19-21, 2012, Kaohsiung, Taiwan. |
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| In
this paper, a hybrid decision forest is constructed by double randomization
of the original training set. In this decision forest, each individual
base decision tree classifiers are incorporated with an additional
classifier model, the ￥emph{Logitboosted} decision stump. In the
first randomization, the resamples to train the decision trees
are extracted; in the second randomization, second set of resamples
are generated from the out-of-bag samples of the first set of resamples.
The boosted decision stumps are constructed on the second resamples.
These extra resamples along with the resamples on which the base
tree classifiers are trained, approximates the original training
set. In this way we are utilizing the full training set to construct
a hybrid decision forest with larger feature space. We have applied
this hybrid decision forest in two real world applications; a)
classifying credit scores, and b) short term extreme rainfall forecast.
The performance of the hybrid decision forest in these two problems
are compared with some well known machine learning methods. Overall
results suggest that the new hybrid decision forest is capable
of yielding commendable predictive performance. |
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| double
randomization; decision forest; real logit boosting; credit
classification; extreme rainfall prediction;
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Times Cited in Web of Science:
Cited in Books:
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