Steps toward artificial intelligence
Computers & thought
Learning in the presence of concept drift and hidden contexts
Machine Learning
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Entropy-based Concept Shift Detection
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Dynamic integration of classifiers for handling concept drift
Information Fusion
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Classifying Data Streams with Skewed Class Distributions and Concept Drifts
IEEE Internet Computing
A Practical Approach to Classify Evolving Data Streams: Training with Limited Amount of Labeled Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Learning with local drift detection
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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
Semi-supervised ensemble learning of data streams in the presence of concept drift
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
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Recently, mining data streams has attracted significant attention and has been considered as a challenging task in supervised classification. Most of the existing methods dealing with this problem assume the availability of entirely labeled data streams. Unfortunately, such assumption is often violated in real-world applications given that obtaining labels is a time-consuming and expensive task, while a large amount of unlabeled instances are readily available. In this paper, we propose a new approach for handling concept-drifting data streams containing labeled and unlabeled instances. First, we use KL divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classifier is learned using the EM algorithm; otherwise, the current classifier is kept unchanged. Our approach is general so that it can be applied with different classification models. Experiments performed with naive Bayes and logistic regression, on two benchmark datasets, show the good performance of our approach using only limited amounts of labeled instances.