Abstract
In this hybrid decision forest each individual base decision tree classifiers are integrated with an additional classifier model, the boosted decision stump. In this boosting, observation weights for subsequent iterations are updated according to the binomial log-likelihood (L2) loss function. 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. We have applied this hybrid decision forest in a real world applications: prediction of short term extreme rainfall. 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.
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Faisal, Z.M., Monira, S.S., Hirose, H. (2013). DF-ReaL2Boost: A Hybrid Decision Forest with Real L2Boosted Decision Stumps. In: Gaol, F. (eds) Recent Progress in Data Engineering and Internet Technology. Lecture Notes in Electrical Engineering, vol 156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28807-4_8
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DOI: https://doi.org/10.1007/978-3-642-28807-4_8
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