The induction of decision trees is a widely-used approach to build classification models that
guarantee high performance and expressiveness. Since a recursive-partitioning strategy …

Abstract Decision Trees (DTs) are a class of supervised learning models that are widely
used for both classification and regression applications. They are well-known for their …

Data classification with decision tree models is an attractive method in data analysis and
data mining. However, compared to other classification methods, the quality of prediction of …

We present a new Decision Tree Classifier (DTC) induction algorithm that produces vastly
more interpretable trees in many situations. These understandable trees are highly relevant …

The interest in interpretable models that are not only accurate but also understandable is
rapidly increasing; often resulting in the machine-learning community turning to decision …

In this paper, some significant efforts on fuzzy oblique decision tree (FODT) have been done
to improve classification accuracy and decrease tree size. Firstly, to eliminate data …

We propose a new oblique decision tree algorithm based on support vector machines. Our
algorithm produces a single model for a multi-class target variable. On the contrary to …

We present an algorithm for learning oblique decision trees, called HHCART (G). Our
decision tree combines learning concepts from two classification trees, HHCART and …

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 …

This study addresses the challenge of generating accurate and compact oblique decision
trees using self-adaptive differential evolution algorithms. Although traditional decision tree …