HHCART: An Oblique Decision Tree

TL;DR

HHCART introduces a novel oblique decision tree algorithm using Householder reflections to efficiently find splits that better capture complex decision boundaries, handling mixed feature types with comparable accuracy to existing methods.

Contribution

The paper presents HHCART, a new decision tree algorithm that employs Householder matrices for efficient oblique splits, improving boundary simplicity and handling mixed features.

Findings

HHCART achieves accuracy comparable to benchmark methods.

HHCART produces trees of similar size to existing algorithms.

The method effectively handles both qualitative and quantitative features.

Abstract

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.