Random Forests
Leo Breiman
- Vol. 45, Iss: 1, pp 5-32
TLDR
Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.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 Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.read more
Citations
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Journal ArticleDOI
Evaluation of variable selection methods for random forests and omics data sets.
TL;DR: Vita is considerably faster than Boruta and thus more suitable for large data sets, but only Boruta can also be applied in low-dimensional settings, while Vita was the most robust approach under a pure null model without any predictor variables related to the outcome.
Journal ArticleDOI
PhiSpy: a novel algorithm for finding prophages in bacterial genomes that combines similarity- and composition-based strategies
TL;DR: A weighted phage detection algorithm, PhiSpy was developed based on seven distinctive characteristics of prophages, i.e. protein length, transcription strand directionality, customized AT and GC skew, the abundance of unique phage words, phage insertion points and the similarity of phage proteins, which leads to the successful prediction of 94% of pro phages in 50 complete bacterial genomes.
Journal ArticleDOI
A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost
TL;DR: An efficient machine learning method, random forests in combination with extreme gradient boosting (XGBoost), is used to establish the data-driven wind turbine fault detection framework that is robust to various wind turbine models including offshore ones in different working conditions.
Proceedings ArticleDOI
Recognising spontaneous facial micro-expressions
TL;DR: A temporal interpolation model together with the first comprehensive spontaneous micro-expression corpus enable the system to accurately recognise these very short expressions and achieves very promising results that compare favourably with the human micro- expression detection accuracy.
Journal ArticleDOI
Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging.
TL;DR: The complexity of lesions segmentation is described, the automatic MS lesion segmentation methods found, and the validation methods applied are reviewed, to evaluate the state of the art in automated multiple sclerosis lesion segmentsation.
References
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Journal ArticleDOI
Bagging predictors
TL;DR: Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.
Proceedings Article
Experiments with a new boosting algorithm
Yoav Freund,Robert E. Schapire +1 more
TL;DR: This paper describes experiments carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems and compared boosting to Breiman's "bagging" method when used to aggregate various classifiers.
Journal ArticleDOI
The random subspace method for constructing decision forests
TL;DR: A method to construct a decision tree based classifier is proposed that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
Journal ArticleDOI
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
TL;DR: In this article, the authors compared the effectiveness of randomization, bagging, and boosting for improving the performance of the decision-tree algorithm C4.5 and found that in situations with little or no classification noise, randomization is competitive with bagging but not as accurate as boosting.
Journal ArticleDOI
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
Eric Bauer,Ron Kohavi +1 more
TL;DR: It is found that Bagging improves when probabilistic estimates in conjunction with no-pruning are used, as well as when the data was backfit, and that Arc-x4 behaves differently than AdaBoost if reweighting is used instead of resampling, indicating a fundamental difference.