Oblique forests

资 源 简 介

Oblique forests

详 情 说 明

Oblique forests represent an advanced variant of random forests that address a key limitation of traditional decision trees: axis-aligned splits. While standard random forests use univariate splits (single-feature thresholds), oblique forests introduce multivariate trees capable of learning optimal split directions through linear discriminative models.

The core innovation lies in how internal nodes determine partitions. Instead of randomly selecting coefficients for feature combinations (as in original oblique random forests), this approach actively learns discriminative linear transformations during training. Each split becomes a hyperplane in the feature space, allowing the model to capture more complex decision boundaries compared to axis-orthogonal splits.

This methodology proves particularly valuable in high-dimensional spaces where features exhibit strong correlations, as the learned linear combinations can better separate classes. The resulting model maintains the ensemble benefits of random forests while offering improved expressiveness through geometrically sophisticated partitions. Implementation typically involves alternating between optimizing split directions and refining the forest structure, often with regularization to prevent overfitting in the linear models.