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
Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions
TL;DR: A newly developed ensemble method, soft rule fit, was used to improve this model and capture non-linear responses of QTL to stresses, enabling the modeling of quantitative trait loci by environment interaction (Q*E), on a genome-wide scale.
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Weed detection in soybean crops using ConvNets
Alessandro dos Santos Ferreira,Daniel Matte Freitas,Gercina Gonçalves da Silva,Hemerson Pistori,Marcelo Theophilo Folhes +4 more
TL;DR: This work achieved above 98% accuracy using Convolutional Neural Networks in the detection of broadleaf and grass weeds in relation to soil and soybean, with an accuracy average between all images above 99%.
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Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping
TL;DR: In this paper, the authors applied support vector machine (SVM), random forest (RF), and genetic algorithm optimized random forests (RFGA) methods to assess groundwater potential by spring locations.
Proceedings ArticleDOI
Crowd Counting with Deep Negative Correlation Learning
TL;DR: This work proposes a new learning strategy to produce generalizable features by way of deep negative correlation learning (NCL), which deeply learn a pool of decorrelated regressors with sound generalization capabilities through managing their intrinsic diversities.
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Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone
Davide Chicco,Giuseppe Jurman +1 more
TL;DR: Analysis of a dataset of 299 patients with heart failure collected in 2015 shows that serum creatinine and ejection fraction are sufficient to predict survival of heart failure patients from medical records, and that using these two features alone can lead to more accurate predictions than using the original dataset features in its entirety.
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.