Random Forests
Leo Breiman
- Vol. 45, Iss: 1, pp 5-32
Reads0
Chats0
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
More filters
Journal ArticleDOI
The consensus molecular subtypes of colorectal cancer
Justin Guinney,Rodrigo Dienstmann,Rodrigo Dienstmann,Xingwu Wang,Xingwu Wang,Aurélien de Reyniès,Andreas Schlicker,Charlotte Soneson,Laetitia Marisa,Paul Roepman,Gift Nyamundanda,Paolo Angelino,Brian M. Bot,Jeffrey S. Morris,Iris Simon,Sarah Gerster,Evelyn Fessler,Felipe De Sousa E Melo,Edoardo Missiaglia,Hena R. Ramay,David Barras,Krisztian Homicsko,Dipen M. Maru,Ganiraju C. Manyam,Bradley M. Broom,Valérie Boige,Beatriz Perez-Villamil,Ted Laderas,Ramon Salazar,Joe W. Gray,Douglas Hanahan,Josep Tabernero,René Bernards,Stephen H. Friend,Pierre Laurent-Puig,Jan Paul Medema,Anguraj Sadanandam,Lodewyk F. A. Wessels,Mauro Delorenzi,Mauro Delorenzi,Scott Kopetz,Louis Vermeulen,Sabine Tejpar +42 more
TL;DR: An international consortium dedicated to large-scale data sharing and analytics across expert groups is formed, showing marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMSs) with distinguishing features.
Proceedings ArticleDOI
A detailed analysis of the KDD CUP 99 data set
TL;DR: A new data set is proposed, NSL-KDD, which consists of selected records of the complete KDD data set and does not suffer from any of mentioned shortcomings.
Journal ArticleDOI
Tracking-Learning-Detection
TL;DR: A novel tracking framework (TLD) that explicitly decomposes the long-term tracking task into tracking, learning, and detection, and develops a novel learning method (P-N learning) which estimates the errors by a pair of “experts”: P-expert estimates missed detections, and N-ex Expert estimates false alarms.
Journal ArticleDOI
Real-time human pose recognition in parts from single depth images
Jamie Shotton,Toby Sharp,Alex Aben-Athar Kipman,Andrew Fitzgibbon,Mark J. Finocchio,Andrew Blake,Mat Cook,Richard Moore +7 more
TL;DR: This work takes an object recognition approach, designing an intermediate body parts representation that maps the difficult pose estimation problem into a simpler per-pixel classification problem, and generates confidence-scored 3D proposals of several body joints by reprojecting the classification result and finding local modes.
Journal ArticleDOI
MissForest—non-parametric missing value imputation for mixed-type data
TL;DR: In this comparative study, missForest outperforms other methods of imputation especially in data settings where complex interactions and non-linear relations are suspected and the out-of-bag imputation error estimates of missForest prove to be adequate in all settings.
References
More filters
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.