Bagging and boosting are two popular ensemble methods in machine learning (ML) that
produce many individual decision trees. Due to the inherent ensemble characteristic of …

Data labels are an integral input to develop machine learning (ML) models. In complex
domains, labels represent the externalized product of complex knowledge. While prior …

As the complexity of machine learning (ML) models increases and their application in
different (and critical) domains grows, there is a strong demand for more interpretable and …

The high performance of tree ensemble classifiers benefits from a large set of rules, which,
in turn, makes the models hard to understand. To improve interpretability, existing methods …

We propose an approach to learning sample embedding for analyzing multi-dimensional
datasets. The basic idea is to extract rules from the given dataset and learn the embedding …

As the digital age progresses, more and more data and information is produced. At the same
time, platforms and services have become increasingly more interconnected. Network …

The 21st century will be heavily impacted by the capability to create value from recorded
data of all kinds. In this regard, industrial manufacturing intuitions increasingly rely on new …

With the wave of emerging technologies sweeping almost all physical printing industries, the
emergence of various electronic resources began to seriously affect the dominance of paper …