On Oblique Random Forests

By Bjoern H Menze, B. Michael Kelm, Daniel Nicolas Splitthoff, Ullrich Köthe, Fred A. Hamprecht. 2011

256 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Molecular Biology

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Citations in the corpus

Mathematical optimization in classification and regression trees (2021, background): “Random Forests (RFs) bag (unpruned) orthogonal trees, and more recently oblique ones (Katuwal et al. 2020; Menze et al. 2011).”
Local Linear Forests (2018, methodology): “Menze et al. (2011) proposed oblique random forests that learn split directions using the results from ridge regression, similar to our work developing splitting rules for local linear forests but more in the spirit of linear discriminant analysis (LDA).”
A random forest guided tour (2015, background): “As noticed by Menze et al. (2011), the feature space separation by orthogonal hyperplanes in random forests results in box-like decision surfaces, which may be advantageous for some data but suboptimal for other, particularly for collinear data with correlated features.”
Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] (2016, methodology): “with ridge regression in each node to obtain the ‘best-split’ [71]; Hybrid oblique random forest [72] with differ-”
Neural Random Forests (2016, background): “…have fewer parameters to tune, but the greedy feature space separation by orthogonal hyperplanes results in typical stair or box-like decision surfaces, which may be advantageous for some data but suboptimal for other, particularly for colinear data with correlated features (Menze et al., 2011).”
Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning (2011, background): “the machine learning, vision and medical imaging literature [9, 15, 24, 29, 33, 35, 52, 53, 54, 56, 58, 59, 60, 61, 65, 70, 80, 83, 88, 102].”
Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning (2012, background): “Recent years have seen an explosion of forest-based techniques in the machine learning, vision and medical imaging literature [9, 15, 25, 31, 35, 37, 58, 59, 65, 67, 68, 69, 74, 79, 89, 92, 97, 110].”
End-to-End Learning of Decision Trees and Forests (2019, background): “Menze et al. (2011) have benchmarked oblique random forests on various binary classiﬁcation problems.”
Detailed 3D Representations for Object Recognition and Modeling (2013, methodology): “In the individual nodes of the decision trees, we use oblique splits that decide based on random hyperplanes of a larger number of randomly chosen dimensions [40], as opposed to the more commonly used axis-aligned (or orthogonal) splits, where node decisions are based on a single feature dimension.”
Extended Isolation Forest (2018, methodology): “Using oblique hyperplanes in tree based methods has also been studied in random forests [12] but again, not in the context of Isolation Forest.”
Local Decorrelation For Improved Pedestrian Detection (2014, background): “Decision trees with oblique splits can more effectively model data with correlated features as the topology of the resulting classifier can better match the natural topology of the data [23].”