Abstract
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 multi-label data streams. In this paper, we propose an improved binary relevance method to take advantage of dependence information among class labels, and propose a dynamic classifier ensemble approach for classifying multi-label concept-drifting data streams. The weighted majority voting strategy is used in our classification algorithm. Our empirical study on both synthetic data set and real-life data set shows that the proposed dynamic classifier ensemble with improved binary relevance approach outperforms dynamic classifier ensemble with binary relevance algorithm, and static classifier ensemble with binary relevance algorithm.
This work is supported by Young Cadreman Supporting Program of Northwest A&F University (01140301). Corresponding author: Zhang Yang.
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Qu, W., Zhang, Y., Zhu, J., Qiu, Q. (2009). Mining Multi-label Concept-Drifting Data Streams Using Dynamic Classifier Ensemble. In: Zhou, ZH., Washio, T. (eds) Advances in Machine Learning. ACML 2009. Lecture Notes in Computer Science(), vol 5828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05224-8_24
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DOI: https://doi.org/10.1007/978-3-642-05224-8_24
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