Fast growing and interpretable oblique trees via logistic regression models

25 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Mathematical optimization in classification and regression trees (2021, methodology): “The first enhancement relates to extending orthogonal splits to oblique, a.k.a. multivariate, splits, with implementations such as OC1 (Murthy et al. 1994), oblique. tree (Truong 2009), and HHCART (Wickramarachchi et al. 2016).”
Optimal classification trees (2017, methodology): “Many approaches to solving this problem have been proposed, including using logistic regression (Truong 2009), support vector machines (Bennett and Blue 1998), simulated annealing (Heath et al 1993), linear discriminant analysis (Loh and Shih 1997; López-Chau et al 2013), Householder transformations”
Optimal trees for prediction and prescription (2018, methodology): “Most other approaches for trees with hyperplane splits apply other methods recursively to generate the splits in the tree, such as logistic regression [116], support vector machines [9], linear discriminant analysis [82, 83], and Householder transformations [120].”
A random forest guided tour (2015, methodology): “As a variant of the original algorithm, Breiman (2001) considered splitting along linear combinations of features (this procedure has been implemented by Truong 2009, in the package obliquetree of the statistical computing environment R).”
Trading Interpretability for Accuracy: Oblique Treed Sparse Additive Models (2015, unknown): “[37] [6, 7, 34] [4] [33, 39] [8] [11] [42] this paper [23]”