Decision trees in random forests use a single feature in non-leaf nodes to split the data. Such splitting results in axis-parallel decision boundaries which may fail to exploit the geometric structure in the data. In oblique decision trees, an oblique hyperplane is employed instead of an axis-parallel hyperplane.
We introduce a new decision forest learning scheme, whose base learners are Minimum Message Length (MML) oblique decision trees. Unlike other tree inference ...
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In this paper, we propose a new ensemble algorithm called decision forests with oblique decision trees. ... Classification And Regression Trees. Wadsworth ...
ODRF implements the well-known Oblique Decision Tree (ODT) and ODT-based Random Forest (ODRF), which uses linear combinations of predictors as partitioning ...
Mar 2, 2021 · An oblique decision tree is a decision tree in which the conditions used to split the data put a constraint over a linear combination of the ...
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Nov 16, 2023 · Oblique decision trees allow performing node splitting based on a combination of features instead of just one feature. This linear combination ...
Decision forests with MML oblique trees [8] is an ensemble classification algorithm which at least matches and sometimes surpasses the " right " / " wrong " ...
Nov 23, 2022 · ODT tends to perform numerically better than CART and requires fewer partitions. In this paper, we show that ODT is consistent for very general ...
[PDF] Decision Forests with Oblique Decision Trees | Semantic Scholar
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A novel weighted averaging scheme is proposed which takes advantage of high probabilistic prediction accuracy produced by MML oblique decision trees, ...
Apr 18, 2024 · Often, decision trees are trained with only axis-aligned conditions. However, oblique splits are more powerful because they can express more ...