Learning in the presence of concept drift and hidden contexts
Machine Learning
Artificial Intelligence Review - Special issue on lazy learning
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
Mining data streams under block evolution
ACM SIGKDD Explorations Newsletter
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International 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
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Systematic data selection to mine concept-drifting data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic integration of classifiers for handling concept drift
Information Fusion
Efficient instance-based learning on data streams
Intelligent Data Analysis
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Multi-agent System Approach to React to Sudden Environmental Changes
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Detecting concept drift using statistical testing
DS'07 Proceedings of the 10th international conference on Discovery science
Combining block-based and online methods in learning ensembles from concept drifting data streams
Information Sciences: an International Journal
Just-in-time adaptive similarity component analysis in nonstationary environments
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
| Hi-index | 0.00 |
We present concept drift responsive method for classifier training for sequential data. Relevant instance selection for training is based on similarity to the target observation. Similarity in space and in time is combined. The algorithm determines an optimal training set size. It can be used plugging in different base classifiers. The proposed algorithm shows the best accuracy in the peer group. The algorithm complexity is reasonable for the field applications.