Mining time-changing data streams
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
Combining proactive and reactive predictions for data streams
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Using additive expert ensembles to cope with concept drift
ICML '05 Proceedings of the 22nd international conference on Machine learning
Proceedings of the 2008 ACM symposium on Applied computing
Active Learning from Data Streams
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
A Practical Approach to Classify Evolving Data Streams: Training with Limited Amount of Labeled Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Stop Chasing Trends: Discovering High Order Models in Evolving Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Integrating Novel Class Detection with Classification for Concept-Drifting Data Streams
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Active learning with evolving streaming data
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
| Hi-index | 0.00 |
We present ActMiner, which addresses four major challenges to data stream classification, namely, infinite length, concept-drift, concept-evolution, and limited labeled data Most of the existing data stream classification techniques address only the infinite length and concept-drift problems Our previous work, MineClass, addresses the concept-evolution problem in addition to addressing the infinite length and concept-drift problems Concept-evolution occurs in the stream when novel classes arrive However, most of the existing data stream classification techniques, including MineClass, require that all the instances in a data stream be labeled by human experts and become available for training This assumption is impractical, since data labeling is both time consuming and costly Therefore, it is impossible to label a majority of the data points in a high-speed data stream This scarcity of labeled data naturally leads to poorly trained classifiers ActMiner actively selects only those data points for labeling for which the expected classification error is high Therefore, ActMiner extends MineClass, and addresses the limited labeled data problem in addition to addressing the other three problems It outperforms the state-of-the-art data stream classification techniques that use ten times or more labeled data than ActMiner.