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The technique employs an ensemble of decision trees to automatically ask the types of questions that astronomers would typically consider when classifying supernova candidates.
Indeed, an ensemble of decision trees is almost always more accurate than a single tree but not necessarily more than a neural network or a support vector machine.
A BDT is an ensemble of decision trees each placing different selection requirements on the individual variables to achieve the best discrimination between 'signal-like' and 'background-like' events.
The Breiman random forest (RF) is an ensemble of decision trees for classification [31].
They developed LogitBoost algorithm which directly approximates the maximum likelihood and applied it to an ensemble of decision trees.
A non-linear regression modeling and prediction based on ensemble of decision trees, which are bootstrapped and aggregated; 4.
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A random forest is an ensemble of decision tree classifiers.
The RF algorithm [ 13] produces an ensemble of decision tree classifiers, where each decision tree is constructed by recursively partitioning the data by feature value tests (forming 'nodes') so as to reduce the entropy of the class label in the resulting partitions ('branches'branches
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization.
We show that single decision trees do not make optimal use of the available information; constructed ensembles of decision trees based on different feature subspaces show significant performance gains (14% parse selection error reduction).
In the last problem related to bioinformatics, the best result obtained by ensembles of DIMLP networks was also significantly better than the best result obtained by ensembles of decision trees.
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