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DF-ReaL2Boost: A Hybrid Decision Forest with
Real L2Boosted Decision Stumps - An Application to Credit Scoring
Classification and Short Term Rainfall Forecasting
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Zaman M. Faisal, Sumi S. Monira, Hideo Hirose
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2011
International Conference on Data Engineering and Internet Technology
(DEIT 2011), 15-17 March 2011, Bali, Indonesia |
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In
this hybrid decision forest each individual base decision tree
classifiers are integrated with an additional classifier model,
the boosted decision stump. In the real boosted decision stump
the class probability estimate is converted using the half-log
ratio to a real valued scale. This value is then used to represent
an observationfs contribution to the final overall model. Furthermore,
observation weights for subsequent iterations are updated according
to the binomial log-likelihood (L2) loss function, which is more
robust against noisy outcomes. This boosted decision stump trained
on the extra samples different than the base tree classifiers (which
are defined as out-of-bag samples). This extra sample along with
the subsample on which the base tree classifiers are trained approximates
the original training set, so in this way we are utilizing the
full training set to construct a hybrid decision forest with larger
feature space. For a better training of the additional boosted
decision stumps we have enlarged the extra sample size by using
small subsample ratios s.t., 0.20, 0.30, 0.40 and 0.50. We have
applied this hybrid decision forest in two real world applications;
a) classifying credit scores and b) short term extreme rainfall
forecast. To check its performance we have also compared the results
with relevant prediction methods of the two applications. Overall
results suggest that the new hybrid decision forest is capable
of yielding commendable predictive performance in both the applications
than most of the methods. |
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decision
forest; real adaboost; logistic loss; credit classification;
rainfall
forecast.
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@
Times Cited in Web of Science:
Cited in Books:
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