On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification
By Gérard Biau, Luc Devroye. 2010
130 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability, Statistical Learning Theory
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Stefan Wager
- Susan Athey
- Gérard Biau
- Erwan Scornet
- Julie Tibshirani
- Lucas K. Mentch
- Robin Genuer
- Sylvain Arlot
In Statistics and Probability
- Stefan Wager
- Susan Athey
- Julie Tibshirani
- Gérard Biau
- Lucas K. Mentch
- Erwan Scornet
- Siyu Zhou
- Robin Genuer
In Statistical Learning Theory
- Stefan Wager
- Susan Athey
- Julie Tibshirani
- Erwan Scornet
- Lucas K. Mentch
- Siyu Zhou
- Robin Genuer
- Sylvain Arlot
Citations in the corpus
- Generalized random forests (2016, methodology): “This adaptive nearest neighbors perspective also underlies several statistical analyses of random forests, including Arlot and Genuer (2014), Biau and Devroye (2010), and Lin and Jeon (2006).”
- Optimal weighted nearest neighbour classifiers (2011, result): “Hall and Samworth (2005) and Biau and Devroye (2010) proved an analogous result for the bagged nearest neighbour classifier to the Stone (1977) result described in the previous paragraph.”
- Getting Better from Worse: Augmented Bagging and a Cautionary Tale of Variable Importance (2020, methodology): “…previous two decades since their inception, numerous studies have sought to establish their important statistical properties including consistency (Biau and Devroye, 2010; Scornet et al., 2015; Klusowski, 2019), asymptotic normality (Mentch and Hooker, 2016; Wager and Athey, 2018), and rates of…”
- A random forest guided tour (2015, background): “The most celebrated theoretical result is that of Breiman (2001), which offers an upper bound on the generalization error of forests in terms of correlation and strength of the individual trees.”
- Analysis of purely random forests bias (2014, background): “Few theoretical results exist on RF, mainly the analysis of Bagging of Bühlmann and Yu (2002)—bagging, introduced by Breiman (1996), can be seen a posteriori as a particular case of RF— and the link between RF and nearest neighbors (Lin and Jeon, 2006; Biau and Devroye, 2010).”