scikit-multiflow

The following papers have been accepted for presentation at the International Joint Conference on Neural Networks (IJCNN): “On Ensemble Techniques for Data Stream Regression” Authors: Heitor Murilo Gomes, Jacob Montiel, Saulo Martiello Mastelini, Bernhard Pfahringer and Albert Bifet “Adaptive XGBoost for Evolving Data Streams” Authors: Jacob Montiel, Rory Mitchell, Eibe Frank, Bernhard Pfahringer, Talel Abdessalem and Albert Bifet

Our tutorial “Machine learning for data streams with scikit-multiflow” has been accepted for presentation in IJCAI-PRICAI 2020! Abstract Data stream mining has gained a lot of attention in recent years as an exciting research topic. However, there is still a gap between the pure research proposals and the practical applications to real-world machine learning problems. The main goal of this tutorial is to introduce attendees to data stream mining theory and practice.

Our tutorial for stream learning with scikit-multiflow has been accepted for presentation at the International Joint Conference on Neural Networks (IJCNN) to take part alongside the IEEE World Congress on Computational Intelligence (WCCI) 2020.

Boosting is an ensemble method that combines base models in a sequential manner to achieve high predictive accuracy. A popular learning …

Data ubiquity highlights the need of efficient and adaptable data-driven solutions. In this paper, we present FAST AND SLOW LEARNING …

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