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With two data generation methods, DBNSim and GeneSim, and two base inference algorithms, DBI and DEI, we can conduct experiments with different combinations of data generation methods and inference algorithms to verify that our boosting algorithms work under all circumstances.
For this reason, we applied AdaBoost [ 14], a classic boosting algorithm, to MSVM-RFE algorithm instead of bootstrapping in this work.
As the same with other algorithms, boosting algorithm also is a family method.
Our combined Boosting with Bagging algorithm emphasizes weaker learner for each boosting run.
We have shown here that they can also be of great benefit to boost combinatorial algorithms such as RISOTTO.
In [4], the authors did not use scale-selection algorithm and used boosting algorithm after extracting multiple MB-LBP features.
Finally, the selected features were classified with a boosted tree algorithm.
The learning algorithm that was used is a meta-classifier based on the boosted stumps algorithm.
The decision tree classifier was built with the use of boosted C5.0 algorithm.
Algorithm 1: RDA-based boosting algorithm.
Therefore, based on the advantages of boosting algorithm, a hybrid forecasting method that combines boosting algorithm with the ARMA-MS model is proposed to improve the forecast accuracy.
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