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With the aid of decision trees, an optimal decision strategy can be developed.
Bag of decision trees.
This results in the faster construction of decision trees.
For a more detailed description of decision trees, see [53].
The global minima of error rate with the change in the number of decision trees may be taken as the optimal number of decision trees.
In this paper, a new algorithm called OPT-2 for optimal pruning of decision trees is introduced.
From a methodology aspect, AdaBoost and RF are both ensembles of decision trees.
As the classifiers, we have tried support vector machine and random forest of decision trees.
It is trained by growing a forest of decision trees using CART methodology.
The main disadvantages of evolutionary induction of decision trees are related to time and space constraints.
The Breiman random forest (RF) is an ensemble of decision trees for classification [31].
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