Breiman, L.: Random Forests. In: Machine Learning 45 (2001), Nr. 1, S. 5-32
[Volltext]
Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to
@article{breiman2001random,
author = {Breiman, Leo},
title = {Random Forests},
journal = {Machine Learning},
publisher = {Kluwer Academic Publishers},
year = {2001},
volume = {45},
number = {1},
pages = {5-32},
url = {http://dx.doi.org/10.1023/A%3A1010933404324},
doi = {10.1023/A:1010933404324},
keywords = {ensemble, classification, forest, learning, random},
abstract = {Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to }
}