This paper presents a survey of evolutionary algorithms that are designed for decision-tree
induction. In this context, most of the paper focuses on approaches that evolve decision …

The symbolic methods have recently regained popularity due to their reasonable
interpretability compared to neural network-based artificial intelligence techniques. The …

Since machine and deep learning have made accurate solutions possible, the search for
explainable predictors has begun. Decision trees are competitive in tasks that require …

The worldwide adoption of mobile devices is raising the value of Mobile Performance
Marketing, which is supported by Demand-Side Platforms (DSP) that match mobile users to …

A regression tree is a type of decision tree that can be applied to solve regression problems.
One of its characteristics is that it may have at least four different node representations; …

In many real world problems, the collected data are not always numeric; rather, the data can
include categorical variables. Inclusion of different types of variables may lead to …

Traditional and ensemble methods are linear models which are considered the most
popular techniques for various learning tasks for the prediction of both nominal and …

Decision-tree induction algorithms are widely used in machine learning applications in
which the goal is to extract knowledge from data and present it in a graphically intuitive way …

Alzheimer's disease is one of the most common forms of dementia affecting millions of
senior people worldwide. In this paper, we develop an Alzheimer's disease prediction model …

The world around us is changing very fast mainly because of spectacular progress in
information and communication technologies. A phenomenon known only from science …