Learning in the presence of concept drift and hidden contexts
Machine Learning
Machine Learning - Special issue on context sensitivity and concept drift
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Towards a Theory of Context Spaces
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
Combining proactive and reactive predictions for data streams
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Tracking Recurrent Concept Drift in Streaming Data Using Ensemble Classifiers
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Tracking Recurring Concepts with Meta-learners
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Tracking recurring contexts using ensemble classifiers: an application to email filtering
Knowledge and Information Systems
Improving the learning of recurring concepts through high-level fuzzy contexts
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
New management operations on classifiers pool to track recurring concepts
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
| Hi-index | 0.00 |
The problem of recurring concepts in data stream classification is a special case of concept drift where concepts may reappear. Although several methods have been proposed that are able to learn in the presence of concept drift, few consider concept recurrence and integration of context. In this work, we extend existing drift detection methods to deal with this problem by exploiting context information associated with learned decision models in situations where concepts reappear. The preliminary experimental results demonstrate the effectiveness of the proposed approach for data stream classification problems with recurring concepts.