160 citations · 169 across the 5 of their papers we have counts for
5 papers · 1 filter
Improving Predictions of Tail-end Labels using Concatenated BioMed-Transformers for Long Medical Documents
Vithya Yogarajan, Bernhard Pfahringer, Tony Smith +1
Multi-label learning predicts a subset of labels from a given label set for an unseen instance while considering label correlations. A known challenge with multi-label classificati…
Predicting COVID-19 Patient Shielding: A Comprehensive Study
Vithya Yogarajan, Jacob Montiel, Tony Smith +1
There are many ways machine learning and big data analytics are used in the fight against the COVID-19 pandemic, including predictions, risk management, diagnostics, and prevention…
River: machine learning for streaming data in Python
Jacob Montiel, Max Halford, Saulo Martiello Mastelini +8
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, performan…
Adaptive XGBoost for Evolving Data Streams
Jacob Montiel, Rory Mitchell, Eibe Frank +3
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 e…
Scikit-Multiflow: A Multi-output Streaming Framework
Jacob Montiel, Jesse Read, Albert Bifet +1
Scikit-multiflow is a multi-output/multi-label and stream data mining framework for the Python programming language. Conceived to serve as a platform to encourage democratization o…