10 papers
Pitfalls of Unlabeled Disagreement-Based Drift Detection in Streaming Tree Ensembles
Lara Sá Neves, Afonso Lourenço, Lizy K. John +1
Detecting concept drift in high-speed data streams remains challenging, particularly when models must operate on unlabeled data and avoid false alarms caused by benign shifts. Whil…
Axle Sensor Fusion for Online Continual Wheel Fault Detection in Wayside Railway Monitoring
Afonso Lourenço, Francisca Osório, Diogo Risca +1
Reliable and cost-effective maintenance is essential for railway safety, particularly at the wheel-rail interface, which is prone to wear and failure. Predictive maintenance framew…
In-context Learning of Evolving Data Streams with Tabular Foundational Models
Afonso Lourenço, João Gama, Eric P. Xing +1
State-of-the-art data stream mining has long drawn from ensembles of the Very Fast Decision Tree, a seminal algorithm honored with the 2015 KDD Test-of-Time Award. However, the eme…
Bridging Streaming Continual Learning via In-Context Large Tabular Models
Afonso Lourenço, João Gama, Eric P. Xing +1
In streaming scenarios, models must learn continuously, adapting to concept drifts without erasing previously acquired knowledge. However, existing research communities address the…
Explainable Anomaly Detection for Industrial IoT Data Streams
Ana Rita Paupério, Diogo Risca, Afonso Lourenço +2
Industrial maintenance is being transformed by the Internet of Things and edge computing, generating continuous data streams that demand real-time, adaptive decision-making under l…
DFDT: Dynamic Fast Decision Tree for IoT Data Stream Mining on Edge Devices
Afonso Lourenço, João Rodrigo, João Gama +1
The Internet of Things generates massive data streams, with edge computing emerging as a key enabler for online IoT applications and 5G networks. Edge solutions facilitate real-tim…