COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Machine Learning
Machine Learning
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining high-speed data streams
Proceedings of the sixth 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
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Sum Versus Vote Fusion in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Induction of Decision Trees
Machine Learning
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Query Learning with Large Margin Classifiers
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
A Probabilistic Active Support Vector Learning Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Active learning using pre-clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Diverse ensembles for active learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Variational Bayes for Continuous Hidden Markov Models and Its Application to Active Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Class noise vs. attribute noise: a quantitative study of their impacts
Artificial Intelligence Review
Batch mode active learning and its application to medical image classification
ICML '06 Proceedings of the 23rd international conference on Machine learning
Suppressing model overfitting in mining concept-drifting data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Learning to Maximize Area Under the ROC Curve
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Nonlinear Boosting Projections for Ensemble Construction
The Journal of Machine Learning Research
Active ensemble learning: Application to data mining and bioinformatics
Systems and Computers in Japan
A Low-Granularity Classifier for Data Streams with Concept Drifts and Biased Class Distribution
IEEE Transactions on Knowledge and Data Engineering
Active Learning from Data Streams
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Learning more about active learning
Communications of the ACM - A Direct Path to Dependable Software
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Active learning with statistical models
Journal of Artificial Intelligence Research
Unsupervised active learning based on hierarchical graph-theoretic clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Mining Data Streams with Labeled and Unlabeled Training Examples
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Vague One-Class Learning for Data Streams
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Robust ensemble learning for mining noisy data streams
Decision Support Systems
Enabling fast prediction for ensemble models on data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Predictive Data Stream Filtering
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Inconsistency-based active learning for support vector machines
Pattern Recognition
GP under streaming data constraints: a case for pareto archiving?
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Active learning for interactive machine translation
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model
International Journal of Data Warehousing and Mining
Benchmarking pareto archiving heuristics in the presence of concept drift: diversity versus age
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Label free change detection on streaming data with cooperative multi-objective genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
| Hi-index | 0.00 |
In this paper, we propose a new research problem on active learning from data streams, where data volumes grow continuously, and labeling all data is considered expensive and impractical. The objective is to label a small portion of stream data from which a model is derived to predict future instances as accurately as possible. To tackle the technical challenges raised by the dynamic nature of the stream data, i.e., increasing data volumes and evolving decision concepts, we propose a classifierensemble-based active learning framework that selectively labels instances from data streams to build a classifier ensemble. We argue that a classifier ensemble's variance directly corresponds to its error rate, and reducing a classifier ensemble's variance is equivalent to improving its prediction accuracy. Because of this, one should label instances toward theminimization of the variance of the underlying classifier ensemble. Accordingly, we introduce a minimum-variance (MV) principle to guide the instance labeling process for data streams. In addition, we derive an optimal-weight calculationmethod to determine the weight values for the classifier ensemble. The MV principle and the optimal weighting module are combined to build an active learning framework for data streams. Experimental results on synthetic and real-world data demonstrate the performance of the proposed work in comparison with other approaches.