Learning in the presence of concept drift and hidden contexts
Machine Learning
Tracking Context Changes through Meta-Learning
Machine Learning - Special issue on multistrategy learning
Machine Learning - Special issue on context sensitivity and concept drift
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting Context When Learning to Classify
ECML '93 Proceedings of the European Conference on Machine Learning
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
Distributed Data Mining in Peer-to-Peer Networks
IEEE Internet Computing
Effective classification of noisy data streams with attribute-oriented dynamic classifier selection
Knowledge and Information Systems
Distributed feature extraction in a p2p setting: a case study
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Dynamic integration of classifiers for handling concept drift
Information Fusion
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Human-Computer Interaction
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Symbiotic Data Mining for Personalized Spam Filtering
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
IEEE Transactions on Knowledge and Data Engineering
Pocket Data Mining: Towards Collaborative Data Mining in Mobile Computing Environments
ICTAI '10 Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Ubiquitous knowledge discovery
On the utility of incremental feature selection for the classification of textual data streams
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
| Hi-index | 0.00 |
Recent advances in ubiquitous devices open an opportunity to apply new data stream mining techniques to support intelligent decision making in the next generation of ubiquitous applications. This paper motivates and describes a novel Context-aware Collaborative data stream mining system CC-Stream that allows intelligent mining and classification of time-changing data streams on-board ubiquitous devices. CC-Stream explores the knowledge available in other ubiquitous devices to improve local classification accuracy. Such knowledge is associated with context information that captures the system state for a particular underlying concept. CC-Stream uses an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the instance space and their context similarity in relation to the current context.