ODTE -- An ensemble of multi-class SVM-based oblique decision trees

TL;DR

ODTE introduces an ensemble of oblique decision trees using SVMs as base classifiers, enabling effective multi-class classification with compact trees and outperforming existing methods across numerous datasets.

Contribution

The paper presents ODTE, a novel ensemble method with a new SVM-based algorithm for oblique decision trees that directly handles multi-class tasks without clustering.

Findings

ODTE outperforms state-of-the-art algorithms on 49 datasets.

Oblique trees learned via STree are more compact than competitors.

Careful hyperparameter tuning enhances ODTE's performance.

Abstract

We propose ODTE, a new ensemble that uses oblique decision trees as base classifiers. Additionally, we introduce STree, the base algorithm for growing oblique decision trees, which leverages support vector machines to define hyperplanes within the decision nodes. We embed a multiclass strategy -- one-vs-one or one-vs-rest -- at the decision nodes, allowing the model to directly handle non-binary classification tasks without the need to cluster instances into two groups, as is common in other approaches from the literature. In each decision node, only the best-performing model SVM -- the one that minimizes an impurity measure for the n-ary classification -- is retained, even if the learned SVM addresses a binary classification subtask. An extensive experimental study involving 49 datasets and various state-of-the-art algorithms for oblique decision tree ensembles has been conducted. Our results show that ODTE ranks consistently above its competitors, achieving significant performance gains when hyperparameters are carefully tuned. Moreover, the oblique decision trees learned through STree are more compact than those produced by other algorithms evaluated in our experiments.