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 …

Despite the large overall beneficial effects of endovascular treatment in patients with acute
ischemic stroke, severe disability or death still occurs in almost one-third of patients. These …

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 …

Decision trees (DT) are a type of data classifiers. A typical classifier works in two phases. In
the first, the learning phase, the classifier is built according to a preexisting data (training) …

Model trees are a particular case of decision trees employed to solve regression problems.
They have the advantage of presenting an interpretable output, helping the end-user to get …

Decision tree (DT) is one of the most popular symbolic machine learning approaches to
classification with a wide range of applications. Decision trees are especially attractive in …

This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is
capable of automatically designing top-down decision-tree induction algorithms. Top-down …

Software effort prediction is an important task within software engineering. In particular,
machine learning algorithms have been widely-employed to this task, bearing in mind their …

Software effort prediction has been a challenge for researchers throughout the years.
Several approaches for producing predictive models from collected data have been …

Decision-tree induction algorithms are widely used in knowledge discovery and data mining,
specially in scenarios where model comprehensibility is desired. A variation of the traditional …