Boosting is an ensemble method that combines base models in a sequential manner to achieve high predictive accuracy. A popular learning algorithm based on this ensemble method is eXtreme Gradient Boosting (XGB). We present an adaptation of XGB for …
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.
A Python library for online machine learning.
One of the ancestors of River. [Superseded by River]