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
Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements.
Nicholas J. Tustison,Philip A. Cook,Arno Klein,Gang Song,Sandhitsu R. Das,Jeffrey T. Duda,Benjamin M. Kandel,Niels M. van Strien,James R. Stone,James C. Gee,Brian B. Avants +10 more
TL;DR: The largest evaluation of automated cortical thickness measures in publicly available data is conducted, comparing FreeSurfer and ANTs measures computed on 1205 images from four open data sets, with parcellation based on the recently proposed Desikan-Killiany-Tourville cortical labeling protocol.
Journal ArticleDOI
PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations.
Jaroslav Bendl,Jan Stourac,Ondrej Salanda,Antonín Pavelka,Eric D. Wieben,Jaroslav Zendulka,Jan Brezovsky,Jiri Damborsky +7 more
TL;DR: This study constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated prediction tools, and returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools.
Journal ArticleDOI
Object-oriented mapping of landslides using Random Forests
TL;DR: A supervised workflow is proposed in this study to reduce manual labor and objectify the choice of significant object features and classification thresholds and resulted in accuracies between 73% and 87% for the affected areas, and approximately balanced commission and omission errors.
Journal ArticleDOI
Efficient Human Pose Estimation from Single Depth Images
Jamie Shotton,Ross Girshick,Andrew Fitzgibbon,Toby Sharp,Mat Cook,Mark J. Finocchio,Richard Moore,Pushmeet Kohli,Antonio Criminisi,Alex Aben-Athar Kipman,Andrew Blake +10 more
TL;DR: Two new approaches to human pose estimation are described, both of which can quickly and accurately predict the 3D positions of body joints from a single depth image without using any temporal information.
Proceedings Article
Sum-product networks: a new deep architecture
Hoifung Poon,Pedro Domingos +1 more
TL;DR: Sum-product networks (SPNs) as discussed by the authors are directed acyclic graphs with variables as leaves, sums and products as internal nodes, and weighted edges, which can be cast as tractable graphical models.
References
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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
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Journal ArticleDOI
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
Eric Bauer,Ron Kohavi +1 more
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