17 citations · 20 across the 5 of their papers we have counts for
5 papers · 1 filter
Detecting Domain Shifts in Myoelectric Activations: Challenges and Opportunities in Stream Learning
Yibin Sun, Nick Lim, Guilherme Weigert Cassales +4
Detecting domain shifts in myoelectric activations poses a significant challenge due to the inherent non-stationarity of electromyography (EMG) signals. This paper explores the det…
Online Isolation Forest
Filippo Leveni, Guilherme Weigert Cassales, Bernhard Pfahringer +2
The anomaly detection literature is abundant with offline methods, which require repeated access to data in memory, and impose impractical assumptions when applied to a streaming c…
CapyMOA: Efficient Machine Learning for Data Streams and Online Continual Learning in Python
Heitor Murilo Gomes, Anton Lee, Nuwan Gunasekara +9
CapyMOA is an open-source Python library for efficient machine learning on data streams and online continual learning. It provides a structured framework for real-time learning, su…
Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing
Guilherme Cassales, Heitor Gomes, Albert Bifet +2
In recent years, the Edge Computing (EC) paradigm has emerged as an enabling factor for developing technologies like the Internet of Things (IoT) and 5G networks, bridging the gap…
Improving the performance of bagging ensembles for data streams through mini-batching
Guilherme Cassales, Heitor Gomes, Albert Bifet +2
Often, machine learning applications have to cope with dynamic environments where data are collected in the form of continuous data streams with potentially infinite length and tra…