Classification and Regression Trees, Bagging, and Boosting

By C. Sutton. 2005

524 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/47a1979c3f7778232c2f038530ff1bb67f235ffa

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Citations in the corpus

Machine Learning Methods Without Tears: A Primer for Ecologists (2008, methodology): “For further discussion on validation techniques and their use in identifying ideal tree size, see Breiman et al. (1984), Bell (1999), Hastie et al. (2001), and Sutton (2005).”
Gradient boosting machines, a tutorial (2013, methodology): “They help to constrain the ﬁtting procedure and thus balance the predictive performance of the resulting model (Sutton, 2005; Zhang and Yu, 2005; Zou and Hastie, 2005).”
Understanding what controls the spatial distribution of fish populations using a multi-model approach (2011, background): “…(ANN, Maravelias et al., 2003, Joy and Death, 2004, Wieland and JarreTeichmann, 1997), regression trees (De'ath and Fabricius, 2000, De'ath, 2002, Sutton et al., 2005, Stratoudakis et al., 1998, Usio, 2007) and their boosted form (Friedman et al., 2000, Friedman and Meulma, 2003, Leathwick et…”
Application of machine learning to construction injury prediction (2016, background): “RF inherits many of the advantages of trees, such as the ability to capture complex nonlinear high-order interactions among predictors, to handle highly dimensional data sets with large numbers of observations, and the robustness to outliers and to the inclusion of irrelevant predictors [63,65].”
Modeling Species Distribution and Change Using Random Forest (2011, background): “…are subject to severe over-fit, (2) due to the sensitivity of CARTs to the complexity (a) parameter, the final tree may not be the optimal solution (Sutton 2005), and (3) CARTs can exhibit high variance and small changes in the data can result in different splits making interpretation somewhat…”
A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring (2017, methodology): “A cost-complexity measure is also employed as follows (Sutton, 2005): C𝛼(𝑇) = 𝐶(𝑇) + 𝛼|𝑇|, (39) where 𝐶(𝑇) is the sum of the squared difference of the response variable and the predicted values for every node in the fitted tree.”
Big data techniques in auditing research and practice: Current trends and future opportunities (2018, unknown): “Tetlock (2007) uses daily content from a popular Wall Street Journal column and finds that when media pessimism is high stock prices decline but then return to fundamentals.”