Abstract Decision Trees (DTs) are predictive models in supervised learning, known not only
for their unquestionable utility in a wide range of applications but also for their interpretability …

Evolutionary Computation (EC) approaches are inspired by nature and solve optimization
problems in a stochastic manner. They can offer a reliable and effective approach to address …

Decision tree classifiers are regarded to be a standout of the most well-known methods to
data classification representation of classifiers. Different researchers from various fields and …

Ensemble learning has gained success in machine learning with major advantages over
other learning methods. Bagging is a prominent ensemble learning method that creates …

Evolution has provided a source of inspiration for algorithm designers since the birth of
computers. The resulting field, evolutionary computation, has been successful in solving …

In this paper, a Pearson's correlation coefficient based decision tree (PCC-Tree) is
established and its parallel implementation is developed in the framework of Map-Reduce …

Classification and regression trees, as well as their variants, are off-the-shelf methods in
Machine Learning. In this paper, we review recent contributions within the Continuous …

Value creation is a major sustainability factor for enterprises, in addition to profit
maximization and revenue generation. Modern enterprises collect big data from various …

A well balanced dataset is very important for creating a good prediction model. Medical
datasets are often not balanced in their class labels. Most existing classification methods …

Industry 4.0 aims to dramatically enhance the productivity of manufacturing technologies
through the collection and analysis of real-time data. This combines the ubiquity of the IoT …