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Found 5 papers, 3 papers with code
Sparse Projection Oblique Randomer Forests
Unfortunately, these extensions forfeit one or more of the favorable properties of decision forests based on axis-aligned splits, such as robustness to many noise dimensions, interpretability, or computational efficiency.
Deep Discriminative to Kernel Density Graph for In- and Out-of-distribution Calibrated Inference
However, these methods are not calibrated for the entire feature space, leading to overconfidence in the case of out-of-distribution (OOD) samples.
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks
In particular, Forests dominate other methods in tabular data, that is, when the feature space is unstructured, so that the signal is invariant to a permutation of the feature indices.
Robust Similarity and Distance Learning via Decision Forests
Many algorithms have been proposed for automated learning of suitable distances, most of which employ linear methods to learn a global metric over the feature space.
MANIFOLD FORESTS: CLOSING THE GAP ON NEURAL NETWORKS
In particular, DFs dominate other methods in tabular data, that is, when the feature space is unstructured, so that the signal is invariant to permuting feature indices.
