6 citations · 12 across the 2 of their papers we have counts for
4 papers
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…
Metropolis-Hastings Algorithms for Estimating Betweenness Centrality in Large Networks
Mostafa Haghir Chehreghani, Talel Abdessalem, and Albert Bifet
Betweenness centrality is an important index widely used in different domains such as social networks, traffic networks and the world wide web. However, even for mid-size networks…
Use of Ensembles of Fourier Spectra in Capturing Recurrent Concepts in Data Streams
Sripirakas Sakthithasan, Russel Pears, Albert Bifet +1
In this research, we apply ensembles of Fourier encoded spectra to capture and mine recurring concepts in a data stream environment. Previous research showed that compact versions…