Decision tree techniques have been widely used to build classification models as such
models closely resemble human reasoning and are easy to understand. This paper …

The goal of supervised learning is to build a concise model of the distribution of class labels
in terms of predictor features. The resulting classifier is then used to assign class labels to …

Fuzzy decision trees (FDTs) have shown to be an effective solution in the framework of fuzzy
classification. The approaches proposed so far to FDT learning, however, have generally …

Over the past decades, machine learning techniques, such as supervised learning,
reinforcement learning, and unsupervised learning, have been increasingly used in the …

Classical classification methods usually assume that pattern recognition models do not
depend on the timing of the data. However, this assumption is not valid in cases where new …

There is an urgent need for a quick screening process that could help neurologists diagnose
and determine whether a patient is epileptic versus simply demonstrating symptoms linked …

The overwhelming amount of data that are now available in any field of research poses new
problems for data mining and knowledge discovery methods. Due to this huge amount of …

Decision trees are one of the most famous methods for solving classification problems,
mainly because of their good interpretability properties. Moreover, due to advances in recent …

A decision tree is a rooted, directed tree akin to a flowchart. Each internal node corresponds
to a partitioning decision, and each leaf node is mapped to a class label prediction. To …

Decision trees are a widely used tool for pattern recognition and data mining. Over the last 4
decades, many algorithms have been developed for the induction of decision trees. Most of …