61 citations · 68 across the 15 of their papers we have counts for
8 papers · 1 filter
AiGAS-dEVL-RC: An Adaptive Growing Neural Gas Model for Recurrently Drifting Unsupervised Data Streams
Maria Arostegi, Miren Nekane Bilbao, Jesus L. Lobo +1
Concept drift and extreme verification latency pose significant challenges in data stream learning, particularly when dealing with recurring concept changes in dynamic environments…
Resilience to the Flowing Unknown: an Open Set Recognition Framework for Data Streams
Marcos Barcina-Blanco, Jesus L. Lobo, Pablo Garcia-Bringas +1
Modern digital applications extensively integrate Artificial Intelligence models into their core systems, offering significant advantages for automated decision-making. However, th…
AiGAS-dEVL: An Adaptive Incremental Neural Gas Model for Drifting Data Streams under Extreme Verification Latency
Maria Arostegi, Miren Nekane Bilbao, Jesus L. Lobo +1
The ever-growing speed at which data are generated nowadays, together with the substantial cost of labeling processes cause Machine Learning models to face scenarios in which data…
Managing the unknown: a survey on Open Set Recognition and tangential areas
Marcos Barcina-Blanco, Jesus L. Lobo, Pablo Garcia-Bringas +1
In real-world scenarios classification models are often required to perform robustly when predicting samples belonging to classes that have not appeared during its training stage.…
On the Connection between Concept Drift and Uncertainty in Industrial Artificial Intelligence
Jesus L. Lobo, Ibai Laña, Eneko Osaba +1
AI-based digital twins are at the leading edge of the Industry 4.0 revolution, which are technologically empowered by the Internet of Things and real-time data analysis. Informatio…
CURIE: A Cellular Automaton for Concept Drift Detection
Jesus L. Lobo, Javier Del Ser, Eneko Osaba +2
Data stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, g…
New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Data
Eric L. Manibardo, Ibai Laña, Jesus L. Lobo +1
This work focuses on classification over time series data. When a time series is generated by non-stationary phenomena, the pattern relating the series with the class to be predict…
LUNAR: Cellular Automata for Drifting Data Streams
Jesus L. Lobo, Javier Del Ser, Francisco Herrera
With the advent of huges volumes of data produced in the form of fast streams, real-time machine learning has become a challenge of relevance emerging in a plethora of real-world a…