160 citations · 184 across the 17 of their papers we have counts for
5 papers · 1 filter
Rebalancing Learning on Evolving Data Streams
Alessio Bernardo, Emanuele Della Valle, Albert Bifet
Nowadays, every device connected to the Internet generates an ever-growing stream of data (formally, unbounded). Machine Learning on unbounded data streams is a grand challenge due…
Spiking Neural Networks and Online Learning: An Overview and Perspectives
Jesus L. Lobo, Javier Del Ser, Albert Bifet +1
Applications that generate huge amounts of data in the form of fast streams are becoming increasingly prevalent, being therefore necessary to learn in an online manner. These condi…
Exploiting a Stimuli Encoding Scheme of Spiking Neural Networks for Stream Learning
Jesus L. Lobo, Izaskun Oregi, Albert Bifet +1
Stream data processing has gained progressive momentum with the arriving of new stream applications and big data scenarios. One of the most promising techniques in stream learning…
Recurring Concept Meta-learning for Evolving Data Streams
Robert Anderson, Yun Sing Koh, Gillian Dobbie +1
When concept drift is detected during classification in a data stream, a common remedy is to retrain a framework's classifier. However, this loses useful information if the classif…
Resource-aware Elastic Swap Random Forest for Evolving Data Streams
Diego Marrón, Eduard Ayguadé, José Ramon Herrero +1
Continual learning based on data stream mining deals with ubiquitous sources of Big Data arriving at high-velocity and in real-time. Adaptive Random Forest ({\em ARF}) is a popular…