Categorizing and mining concept drifting data streams
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
An Aggregate Ensemble for Mining Concept Drifting Data Streams with Noise
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Active learning from stream data using optimal weight classifier ensemble
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Building a new classifier in an ensemble using streaming unlabeled data
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Editorial: Classifying text streams by keywords using classifier ensemble
Data & Knowledge Engineering
Effective sentiment stream analysis with self-augmenting training and demand-driven projection
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Distributed learning with data reduction
Transactions on computational collective intelligence IV
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
Classification and novel class detection in data streams with active mining
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Active learning for interactive machine translation
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Learning from data streams with only positive and unlabeled data
Journal of Intelligent Information Systems
Feedback-driven multiclass active learning for data streams
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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In this paper, we address 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 newly arrived instances as accurate as possible. In order to tackle the challenges raised by data streams' dynamic nature, we propose a classifier ensembling based active learning framework which selectively labels instances from data streams to build an accurate classifier. A Minimal Variance principle is introduced to guide instance labeling from data streams. In addition, a weight updating rule is derived to ensure that our instance labeling process can adaptively adjust to dynamic drifting concepts in the data. Experimental results on synthetic and real-world data demonstrate the performances of the proposed efforts in comparison with other simple approaches. *