Points of Significance: Classification and regression trees

By Leo Breiman, Richard A. Olshen. 2017

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Citations in the corpus

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, methodology): “Full decision tree optimization is known to be an NP-complete problem (Laurent and Rivest, 1976), and heuristic greedy splitting and pruning procedures have been the major type of approach since the 1980s to grow decision trees (Breiman et al., 1984; Quinlan, 1993; Loh and Shih, 1997; Mehta et al., ”
Survey on Automated Machine Learning (2019, methodology): “2 Random Forests Random forest regression (Breiman, 2001) is an ensemble method consisting of multiple regression trees (Breiman et al., 1984).”
Computing Optimal Decision Sets with SAT (2020, background): “Later, [34] considered decision sets as a more explainable alternative to decision trees [10] and decision lists [50].”
Optimal Decision Trees for the Algorithm Selection Problem: Integer Programming Based Approaches (2019, methodology): “Thus, many greedy heuristics have been proposed, such as ID3 (Quinlan, 1986), C4.5 (Quinlan, 1993) and CART (Breiman et al., 1984).”
Deep Weighted Averaging Classifiers (2018, background): “This category includes classic models that can easily be simulated by humans, such as decision lists, as well as sparse linear models, where the prediction is based on a weighted sum of features [6, 24, 29, 49, 50, 56].”
Deep Neural Decision Trees (2018, methodology): “Decision tree (DT) based methods, such as C4.5 (Quinlan, 1993) and CART (Breiman et al., 1984), have a clear advantage in this aspect, as one can easily follow the structure of the tree and check exactly how a prediction is made.”
Efficient Non-greedy Optimization of Decision Trees (2015, background): “They grow a tree one node at a time following procedures laid out decades ago by frameworks such as ID3 [22] and CART [6].”
Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes (2015, methodology): “However, the tree structure of decision lists suggests a greedy algorithm in the spirit of classification and regression trees (CART, Breiman et al., 1984).”
Optimal randomized classification trees (2021, methodology): “CART [32] is a popular algorithm for growing decision trees which, due to the complexity and the available computer technology at the time it was introduced, is based on a simple greedy and sequential partitioning procedure.”
Real-Time Multi-scale Action Detection from 3D Skeleton Data (2015, methodology): “Embedded methods [4, 5] embed the feature selection process into the classifier learning process.”
Estimation of soil moisture using decision tree regression (2019, background): “The values of the input variable(s) consider a particular function in the training stage (Loh, 2011).”