This article provides a birds-eye view on the role of decision trees in machine learning and
data science over roughly four decades. It sketches the evolution of decision tree research …

Concept drift describes unforeseeable changes in the underlying distribution of streaming
data overtime. Concept drift research involves the development of methodologies and …

The problem of concept drift has gained a lot of attention in recent years. This aspect is key
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …

River is a machine learning library for dynamic data streams and continual learning. It
provides multiple state-of-the-art learning methods, data generators/transformers …

Class imbalance poses new challenges when it comes to classifying data streams. Many
algorithms recently proposed in the literature tackle this problem using a variety of data …

scikit-multiflow is a framework for learning from data streams and multi-output learning in
Python. Conceived to serve as a platform to encourage the democratization of stream …

Incremental learning, online learning, and data stream learning are terms commonly
associated with learning algorithms that update their models given a continuous influx of …

Portfolio formation in financial markets is the task of not taking non-necessary risks.
Quantitative investment powered by machine learning has opened many new opportunities …

A hands-on approach to tasks and techniques in data stream mining and real-time analytics,
with examples in MOA, a popular freely available open-source software framework. Today …

Predictive modeling in healthcare continues to be an active actuarial research topic as more
insurance companies aim to maximize the potential of Machine Learning (ML) approaches …