Albert Bifet

TL;DR: In this article, the authors present a comprehensive overview of the use of Spiking Neural Networks for online learning in non-stationary data streams and propose a new algorithm to adapt to these changes as fast as possible, while maintaining good performance scores.

TL;DR: River is a machine learning library for dynamic data streams and continual learning that is the result from the merger of the two most popular packages for stream learning in Python: Creme and scikit-multiflow.

TL;DR: The Enhanced Concept Profiling Framework is proposed, which aims to recognise recurring concepts and reuse a classifier trained previously, enabling accurate classification immediately following a drift, and classifies significantly more accurately on synthetic datasets with recurring concepts.

TL;DR: In this paper, the authors proposed a flexible ensemble algorithm for fair decision-making in the more challenging context of evolving online settings based on using online component classifiers and updating them according to the current distribution, that also accounts for fairness and a single hyper-parameter that alters fairness-accuracy balance.

TL;DR: This work presents the Adaptive Random Forest with Resampling (ARFRE), which is a classifier designed to deal with imbalanced datasets and shows that the proposed method can considerably improve the performance of the minority class(es) while avoiding degrading the performance in the majority class.