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 …

In recent years, reinforcement learning (RL) techniques have achieved great success in
many different applications. However, their heavy reliance on complex deep neural …

Imbalanced data classification is one of the most challenging problems in data mining. In
this kind of problems, we have two types of classes: the majority class and the minority one …

Classification of imbalanced multi-class data is still so far one of the most challenging issues
in machine learning and data mining. This task becomes more serious when classes …

The growing demand for complex machine learning models has increased the use of black-
box models, such as random forests and artificial neural networks, posing significant …

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 …

This paper proposes a novel classification technology—fuzzy rule-based oblique decision
tree (FRODT). The neighborhood rough sets-based FAST feature selection (NRS_FS_FAST) …

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

In this chapter, the application of a differential evolution-based approach to induce oblique
decision trees (DTs) is described. This type of decision trees uses a linear combination of …