Fifty Years of Classification and Regression Trees

By Wei-Yin Loh. 2014

629 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

Mathematical optimization in classification and regression trees (2021, methodology): “Classification and regression trees (Loh 2014) are state-of-the-art methods based on recursive partitioning (Hastie et al.”
Predicción y selección de variables con bosques aleatorios en presencia de variables correlacionadas (2020, background): “Sin embargo, para una rápida revisión conceptual de otras propuestas relacionadas puede referirse a Loh (2011, 2014).”
Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations (2020, background): “Truly rule-based classifiers [38, 29], logic circuits [26], or probabilistic approaches [5, 37, 19, 30], are principally viable alternatives for the Set Transformer, though it remains preferable for this module to handle unordered sets.”
A review on longitudinal data analysis with random forest (2022, neutral): “See [33] for a review on different tree construction approaches and related discussions on extensions for longitudinal data analysis.”
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, background): “They can be used for learning classification models (with discrete value predictions) and regression models (with continuous value predictions) [68].”
Dropout vs. batch normalization: an empirical study of their impact to deep learning (2020, methodology): “When carrying out learning on the training data, conventional machine learning algorithms, such as nearest neighbor classification [31], decision trees [19], multilayer perceptrons [26], and support vector machines [3], require each training instance (or sample) to be represented using some feature ”
Recent advances in decision trees: an updated survey (2022, background): “Several great reviews have already been written on the subject, such as the ones by Murthy (1998), Rokach and Maimon (2007), Kotsiantis (2013) and Loh (2014), however, the last decade of research remains uncovered.”
Classification Based on Decision Tree Algorithm for Machine Learning (2021, neutral): “5), Classification And Regression Tree(CART) [44], CHi-squared Automatic Interaction Detector(CHAID) [45], Multivariate Adaptive Regression Splines (MARS) [46], Generalized, Unbiased, Interaction Detection and Estimation (GUIDE), Conditional Inference Trees (CTREE) [47],[48], Classification Rule wit”
Partykit: a modular toolkit for recursive partytioning in R (2015, methodology): “The particularly influential algorithms include CART (classification and regression trees, Breiman et al., 1984), C4.5 (Quinlan, 1993), QUEST/GUIDE (Loh and Shih, 1997; Loh, 2002), and CTree (Hothorn et al., 2006) among many others (see Loh, 2014, for a recent overview).”
Computational design optimization of concrete mixtures: A review (2018, background): “around the time that other machine learning algorithms were first being developed [65].”
Decision tree methods: applications for classification and prediction (2015, background): “Lohand and Strobl [18,19] provided a comprehensive review of the statistical literature of classification tree methods that may be useful for readers who want to learn more about the statistical theories behind the decision tree method.”