Learning in the presence of concept drift and hidden contexts
Machine Learning
BoosTexter: A Boosting-based Systemfor Text Categorization
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
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A maximal figure-of-merit learning approach to text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A family of additive online algorithms for category ranking
The Journal of Machine Learning Research
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
Dynamic Classifier Selection for Effective Mining from Noisy Data Streams
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
A Unified Model for Multilabel Classification and Ranking
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
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The problem of mining single-label data streams has been extensively studied in recent years. However, not enough attention has been paid to the problem of mining multilabel data streams. In this paper, a weighted voting ensemble approach is proposed to tackle this problem. We partition the incoming data stream into sequential chunks, and use binary relevance method to transform each chunk into a set of single-label chunks, which could be learned by binary classification algorithm. We train an ensemble of classifiers from the transformed chunks, and the classifiers in the ensemble are weighted based on their expected classification accuracy on the test data under the time-evolving environment. We also proposed a method for simulating multilabel data stream with concept drifting. Our empirical study on synthetic data set shows that the proposed approach has substantial advantage over majority voting ensemble approach.