# HHCART: An Oblique Decision Tree

arXiv: https://arxiv.org/abs/1504.03415 · HTML: https://mlsanity.com/paper/1504.03415/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2015.

## Built on by (most cited first, 13 of 13)

- [Margin Optimal Classification Trees](https://mlsanity.com/paper/2210.10567/) (2022)
- [Mixed integer linear optimization formulations for learning optimal binary classification trees](https://mlsanity.com/paper/2206.04857/) (2022)
- [Can a Single Tree Outperform an Entire Forest?](https://mlsanity.com/paper/2411.17003/) (2024)
- [OF-AE: Oblique Forest AutoEncoders](https://mlsanity.com/paper/2301.00880/) (2023)
- [Multiple Instance Learning with Trainable Decision Tree Ensembles](https://mlsanity.com/paper/2302.06601/) (2023)
- [Correlation and Unintended Biases on Univariate and Multivariate Decision Trees](https://mlsanity.com/paper/2312.01884/) (2023)
- [The Conditioning Bias in Binary Decision Trees and Random Forests and Its Elimination](https://mlsanity.com/paper/2312.10708/) (2023)
- [FoLDTree: A ULDA-Based Decision Tree Framework for Efficient Oblique Splits and Feature Selection](https://mlsanity.com/paper/2410.23147/) (2024)
- [A novel gradient-based method for decision trees optimizing arbitrary differential loss functions](https://mlsanity.com/paper/2503.17855/) (2025)
- [CART-ELC: Oblique Decision Tree Induction via Exhaustive Search](https://mlsanity.com/paper/2505.05402/) (2025)
- [Even Faster Hyperbolic Random Forests: A Beltrami-Klein Wrapper Approach](https://mlsanity.com/paper/2506.04360/) (2025)
- [A Data-Driven Approach to State Construction in Markov Models](https://mlsanity.com/paper/2608.21480/) (2026)
- [Discovery of fully efficient fault indicators along a data-based diagnosis process](https://mlsanity.com/paper/2609.28087/) (2026)

## Abstract (arXiv)

Decision trees are a popular technique in statistical data classification. They recursively partition the feature space into disjoint sub-regions until each sub-region becomes homogeneous with respect to a particular class. The basic Classification and Regression Tree (CART) algorithm partitions the feature space using axis parallel splits. When the true decision boundaries are not aligned with the feature axes, this approach can produce a complicated boundary structure. Oblique decision trees use oblique decision boundaries to potentially simplify the boundary structure. The major limitation of this approach is that the tree induction algorithm is computationally expensive. In this article we present a new decision tree algorithm, called HHCART. The method utilizes a series of Householder matrices to reflect the training data at each node during the tree construction. Each reflection is based on the directions of the eigenvectors from each classes' covariance matrix. Considering axis parallel splits in the reflected training data provides an efficient way of finding oblique splits in the unreflected training data. Experimental results show that the accuracy and size of the HHCART trees are comparable with some benchmark methods in the literature. The appealing feature of HHCART is that it can handle both qualitative and quantitative features in the same oblique split.