Given the recent trend in Data Science (DS) and Sports Analytics, an opportunity has arisen
for utilizing Machine Learning (ML) and Data Mining (DM) techniques in sports. This paper …

Decision trees have the particularity of being machine learning models that are visually easy
to interpret and understand. Therefore, they are primarily suited for sensitive domains like …

State-of-the-art decision tree methods apply heuristics recursively to create each split in
isolation, which may not capture well the underlying characteristics of the dataset. The …

Learning a decision tree from data is a difficult optimization problem. The most widespread
algorithm in practice, dating to the 1980s, is based on a greedy growth of the tree structure …

Decision tree induction is a simple, however powerful learning and classification tool to
discover knowledge from the database. The volume of data in databases is growing to quite …

Smart cities connect people and places using innovative technologies such as Data Mining
(DM), Machine Learning (ML), big data, and the Internet of Things (IoT). This paper presents …

Sports Analytics is an emerging research area with several applications in a variety of fields.
These could be, for example, the prediction of an athlete's or a team's performance, the …

The predictive performance of a random forest ensemble is highly associated with the
strength of individual trees and their diversity. Ensemble of a small number of accurate and …

For the past 30 years, decision tree methods have been one of the most widely-used
approaches in machine learning across industry and academia, due in large part to their …

Ever since the first cities were created, they have been dependent on technology to sustain
life. The smart city paradigm integrates advanced monitoring, sensing, communication, and …