COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Tracking Drifting Concepts By Minimizing Disagreements
Machine Learning - Special issue on computational learning theory
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
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
Machine Learning
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Effective Learning in Dynamic Environments by Explicit Context Tracking
ECML '93 Proceedings of the European Conference on Machine Learning
Incremental Learning with Support Vector Machines
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Systematic data selection to mine concept-drifting data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
On demand classification of data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Active learning using pre-clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Decision Tree Evolution Using Limited Number of Labeled Data Items from Drifting Data Streams
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Using multiple windows to track concept drift
Intelligent Data Analysis
Online classification of nonstationary data streams
Intelligent Data Analysis
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
StreamMiner: a classifier ensemble-based engine to mine concept-drifting data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Active learning with statistical models
Journal of Artificial Intelligence Research
Editorial: Classifying text streams by keywords using classifier ensemble
Data & Knowledge Engineering
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 |
Mining time-changing data streams is of great interest. The fundamental problems are how to effectively identify the significant changes and organize new training data to adjust the outdated model. In this paper, we propose an active learning system to address these issues. Without need knowing any true labels of the new data, we devise an active approach to detecting the possible changes. Whenever the suspected changes are indicated, it exploits a light-weight uncertainty sampling algorithm to choose the most informative instances to label. With these labeled instances, it further tests the truth of the suspected changes. If the changes indeed cause significant performance deterioration of the current model, it evolves the old model. Thus, our method is sensitive to significant changes and robust to noisy changes, and can quickly adapt to concept-drift. Experimental results from both synthetic and real-world data confirm the advantages of our system.