4 citations · 4 across the 6 of their papers we have counts for
6 papers · 1 filter
Towards Stream Learning on Embedded Systems: Benchmarking the Memory Consumption of Stream Learning Methods
Sebastian Buschjäger, Nuwan Gunasekara, Heitor Murilo Gomes
Stream learning is commonly evaluated through predictive performance and adaptation to concept drift. However, sustained operation of a stream learner also requires predictable and…
A Framework for Evaluating and Benchmarking Concept Drift Detection Methods
Vitor Cerqueira, Heitor Murilo Gomes, Marco Heyden +2
Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance. Despite the proliferation of drift detection methods, p…
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…
Evaluation for Regression Analyses on Evolving Data Streams
Yibin Sun, Heitor Murilo Gomes, Bernhard Pfahringer +1
The paper explores the challenges of regression analysis in evolving data streams, an area that remains relatively underexplored compared to classification. We propose a standardiz…
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…
Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysis
Yibin Sun, Heitor Murilo Gomes, Bernhard Pfahringer +1
This paper introduces a group of novel datasets representing real-time time-series and streaming data of energy prices in New Zealand, sourced from the Electricity Market Informati…