Instance-Based Learning Algorithms
Machine Learning
Learning in the presence of concept drift and hidden contexts
Machine Learning
Machine Learning - Special issue on context sensitivity and concept drift
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Relevant Data Expansion for Learning Concept Drift from Sparsely Labeled Data
IEEE Transactions on Knowledge and Data Engineering
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Lacking Labels in the Stream: Classifying Evolving Stream Data with Few Labels
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Mining Data Streams with Labeled and Unlabeled Training Examples
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Tracking recurring contexts using ensemble classifiers: an application to email filtering
Knowledge and Information Systems
The Journal of Machine Learning Research
Mining concept-drifting data streams containing labeled and unlabeled instances
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
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
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Increasing access to very large and non-stationary datasets in many real problems has made the classical data mining algorithms impractical and made it necessary to design new online classification algorithms. Online learning of data streams has some important features, such as sequential access to the data, limitation on time and space complexity and the occurrence of concept drift. The infinite nature of data streams makes it hard to label all observed instances. It seems that using the semi-supervised approaches have much more compatibility with the problem. So in this paper we present a new semi-supervised ensemble learning algorithm for data streams. This algorithm uses the majority vote of learners for the labeling of unlabeled instances. The empirical study demonstrates that the proposed algorithm is comparable with the state-of-the-art semi-supervised online algorithms.