Improving Random Forests

Robnik-Sikonja, Marko

doi:10.1007/978-3-540-30115-8_34

Improving Random Forests

Marko Robnik-Sikonja

ECML-PKDD 2004 pp. 359-370

doi:10.1007/978-3-540-30115-8_34 /ecmlpkdd/2004/robniksikonja2004ecml-improving/

Abstract

Random forests are one of the most successful ensemble methods which exhibits performance on the level of boosting and support vector machines. The method is fast, robust to noise, does not overfit and offers possibilities for explanation and visualization of its output. We investigate some possibilities to increase strength or decrease correlation of individual trees in the forest. Using several attribute evaluation measures instead of just one gives promising results. On the other hand replacement of ordinary voting with voting weighted with margin achieved on most similar instances gives improvements which are statistically highly significant over several data sets.

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Cite

Text

Robnik-Sikonja. "Improving Random Forests." European Conference on Machine Learning, 2004. doi:10.1007/978-3-540-30115-8_34

Markdown

[Robnik-Sikonja. "Improving Random Forests." European Conference on Machine Learning, 2004.](https://mlanthology.org/ecmlpkdd/2004/robniksikonja2004ecml-improving/) doi:10.1007/978-3-540-30115-8_34

BibTeX

@inproceedings{robniksikonja2004ecml-improving,
  title     = {{Improving Random Forests}},
  author    = {Robnik-Sikonja, Marko},
  booktitle = {European Conference on Machine Learning},
  year      = {2004},
  pages     = {359-370},
  doi       = {10.1007/978-3-540-30115-8_34},
  url       = {https://mlanthology.org/ecmlpkdd/2004/robniksikonja2004ecml-improving/}
}